USRE39977E1 - Near infrared chemical imaging microscope - Google Patents

Near infrared chemical imaging microscope Download PDF

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USRE39977E1
USRE39977E1 US11/103,423 US10342305A USRE39977E US RE39977 E1 USRE39977 E1 US RE39977E1 US 10342305 A US10342305 A US 10342305A US RE39977 E USRE39977 E US RE39977E
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near infrared
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Patrick J. Treado
Matthew Nelson
Scott Keitzer
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ChemImage Technologies LLC
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28Investigating the spectrum
    • G01J3/2823Imaging spectrometer
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/06Means for illuminating specimens
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/0004Microscopes specially adapted for specific applications
    • G02B21/0016Technical microscopes, e.g. for inspection or measuring in industrial production processes

Definitions

  • the present invention is related to near-infrared (NIR) microscopes for spectroscopic and image analysis, and, in particular, to microscopes useful for both NIR spectroscopy, NIR chemical imaging and NIR volumetric chemical imaging.
  • NIR near-infrared
  • NIR spectroscopy is a mature, non-contact, non-destructive analytical characterization tool that has been widely applied to a broad range of materials.
  • the NIR region of the electromagnetic spectrum encompasses radiation with wavelength of 0.78 to 2.5 ⁇ m (12,800 to 4,000 cm ⁇ 1 ).
  • NIR spectra result from the overtone and combination bands of fundamental mid-infrared (MIR) bands.
  • MIR fundamental mid-infrared
  • NIR is used to rapidly obtain both qualitative and quantitative information about the molecular makeup of a material.
  • Digital imaging provides a means to obtain optical (i.e., spatial—morphological, topographical, etc.) information about a material.
  • NIR chemical imaging combines NIR spectroscopy and digital imaging for the molecular-specific analysis of materials.
  • a NIR chemical imaging microscope apparatus employing NIR absorption molecular spectroscopy for materials characterization is disclosed.
  • NIR microscopes are used to obtain NIR absorption, transmittance or reflectance spectra (e.g., NIR microspectra) from samples ranging in size between 1 and 1000 ⁇ m. These instruments are typically equipped with a digital camera to visually locate a region of interest on a sample upon which a NIR light beam from a Fourier transform (FT) spectrometer is focused. Reflective optics are used to direct the transmitted or reflected light from the sample to a NIR detector. The output is a NIR absorption spectrum collected in transmittance or reflectance mode.
  • FT Fourier transform
  • NIR chemical imaging can be considered an extension of NIR microspectroscopy.
  • the NIR light beam is focused onto the surface of a sample or apertured to illuminate a small region of a sample and a spectrum from each spatial position is collected. Images are obtained by rastering the sample through the focused or apertured NIR light beam and the spectra recorded are then reconstructed to form an image.
  • point scanning produces images based on NIR contrast, long experimental times are common since the duration of the experiment is proportional to the number of image pixels.
  • point scan images are captured at low image definition, which relates directly to the limited utility of the technique as an imaging tool for the routine assessment of material morphology.
  • the spatial resolution of the image is limited by the size of the NIR illumination spot on the sample (no less than 1 ⁇ m) and the rastering mechanism, which requires the use of moving mechanical parts that are challenging to operate reproducibly.
  • NIR imaging cameras have been used in photography for decades. Until recently, however, it has not been easily accessible to those not versed in traditional photographic processes.
  • optical filters e.g., cold filters
  • CCDs charge-coupled devices
  • camcorders can be used to sense NIR light out to around 1100 nm.
  • Other regions of the NIR spectrum can be viewed using devices such as indium gallium arsenide (InGaAs—0.9 ⁇ m to 1.7 ⁇ m) and indium antimonide (InSb—1.0 ⁇ m to 5.0 ⁇ m) focal plane array (FPA) detectors.
  • InSb indium antimonide
  • FPA focal plane array
  • dielectric interference filters in combination with NIR FPAs is one method in which chemical information can be obtained from a sample.
  • a NIR light beam is defocused to illuminate a wide field of view and the reflected or transmitted light from the illuminated area is imaged onto a two-dimensional NIR detector.
  • a selection of discrete dielectric interference filters provided in a filter wheel, or a linearly variable or circularly variable format can be positioned in front of a broadband NIR light source, or in front of the NIR FPA itself in order to collect NIR wavelength resolved images.
  • the use of several fixed bandpass filters is required to access the entire NIR spectrum.
  • the spatial resolution of the NIR image approaches that of the optical microscope, while spectral resolution of several nanometers has been demonstrated.
  • dielectric filter approach Key limitations of the dielectric filter approach include the need for a multitude of discrete filters to provide appreciable free spectral range, or the reliance on moving mechanical parts in employing continuously tunable dielectric interference filters as a requirement to form wavelength resolved images. While moving mechanical assemblies can be engineered they add cost and complexity to NIR chemical imaging systems. Alternatives to moving mechanical assemblies are generally more cost effective and provide performance advantages.
  • AOTFs Acoustic-optic tunable filters
  • the AOTF is a solid-state device that is capable of functioning from the UV to the mid-IR depending on the choice of the filter's crystal material. Operation of the AOTF is based on the interaction of light with a traveling acoustic sound wave in an anisotropic crystal medium. The incident light is diffracted with a narrow spectral bandpass when an rf signal is applied to the device. By changing the applied rf frequency under computer control the spectral passband can be tuned rapidly with the benefit of non-moving parts.
  • AOTFs For use in NIR chemical imaging, AOTFs have distinct limitations. AOTFs have imaging performance that is degraded appreciably from diffraction-limited conditions due to dispersion effects and image shifting effects. Furthermore, AOTFs suffer from temperature instability and exhibit nonlinear properties that complicate their use as imaging spectrometers.
  • NIR chemical imaging technology development has been to develop a NIR imaging technique that combines diffraction-limited spatial resolution with high spectral resolution.
  • NIR chemical imaging techniques have only recently achieved a degree of technological maturity that allow the collection of high resolution (spectral and spatial) data with the advent of the liquid crystal (LC) imaging spectrometers.
  • LC devices provide diffraction-limited spatial resolution.
  • the spectral resolution of the LC imaging spectrometer is comparable to that provided by dispersive monochromator and Fourier transform interferometers.
  • LC technology provides high out of band rejection, broad free spectral range, moderate transmittance, high overall etendue and highly reproducible random access computer controlled tuning.
  • LC imaging spectrometers allow NIR chemical images of samples to be recorded at discrete wavelengths (energies). A spectrum is generated corresponding to thousands of spatial locations at the sample surface by tuning the LC imaging spectrometer over a range of wavelengths and collecting NIR images systematically. Contrast is generated in the images based on the relative amounts of NIR absorption, transmittance or reflectance that is generated by the different species located throughout the sample. Since a high quality NIR spectrum is generated for each pixel location, a wide variety of chemometric analysis tools, both univariate and multivariate, can be applied to the NIR image data to extract pertinent information.
  • Correlative multivariate routines are particularly powerful when applied to chemical images collected from samples intentionally seeded with a known standard material. This approach of incorporating calibration standards within an image field of view can be extended to quantitative chemical image analysis.
  • digital image analysis procedures can also be applied to high image quality NIR chemical images to perform routine particle analysis in both two (2D) and three (3D) spatial dimensions. Volumetric 3D NIR chemical image and analysis can be performed very effectively using numerical deconvolution computational strategies.
  • the microscope design uses NIR optimized liquid crystal (LC) imaging spectrometer technology for wavelength selection.
  • the NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector with or without the use of a tube lens.
  • An integrated parfocal analog color CCD detector provides real-time sample positioning and focusing.
  • the color image and the NIR image are fused in software.
  • the NIR microscope may be used as a volumetric imaging instrument through the means of moving the sample through focus, collecting images at varying focal depths and reconstructing a volumetric image of the sample in software, or through the means of keeping the sample fixed and changing the wavelength dependent depth of penetration in conjunction with a refractive tube lens with a well characterized chromatic effect.
  • the output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic.
  • a Chemical Imaging Addition Method seeds the sample with a material of known composition, structure and/or concentration and then generates the NIR image suitable for qualitative and quantitative analysis.
  • the microscope generates NIR chemical image data that is analyzed and visualized using chemical image analysis software in a systematic and comprehensive manner. While this invention has been demonstrated on a microscope optic platform, the novel concepts are also applicable to other image gathering platforms, namely fiberscopes, macrolens systems and telescopes.
  • FIG. 1 shows a schematic diagram of the near-infrared (NIR)chemical imaging microscope.
  • FIG. 2 shows a diagram of the chemical imaging data analysis cycle performed in software.
  • FIG. 3 is a digital brightfield image of a CdZnTe semiconductor material decorated with tellurium inclusions.
  • FIG. 4 an NIR microscopic transmittance image of a CdZnTe semiconductor material decorated with tellurium inclusions.
  • FIG. 5A illustrates a raw NIR image frame of a CdZnTe wafer sample.
  • FIG. 5B illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too low.
  • FIG. 5C illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too high.
  • FIG. 5D illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set to an intermediate level.
  • FIG. 6A is the original raw image of four adjacent regions of interest on a CdZnTe wafer.
  • FIG. 6B is the background-corrected image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6 A.
  • FIG. 6C is the binarized image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6 A.
  • FIG. 7 is a three-dimensional view of tellurium inclusions in a CdZnTe wafer.
  • the NIR chemical imaging microscope combines in a single platform a NIR optimized refractive optical microscope base, which is equipped with NIR optimized infinity-corrected microscope objectives, an automated XYZ translational microscope stage and quartz tungsten halogen (QTH) lamps to secure and illuminate samples for NIR spectroscopy and imaging, an analog color charge-coupled device (CCD) detector for ordinary optical image collection and digital image collection, a NIR LC imaging spectrometer for NIR chemical image wavelength selection and a room temperature or optionally cooled NIR FPA for NIR image capture.
  • NIR optimized refractive optical microscope base which is equipped with NIR optimized infinity-corrected microscope objectives, an automated XYZ translational microscope stage and quartz tungsten halogen (QTH) lamps to secure and illuminate samples for NIR spectroscopy and imaging, an analog color charge-coupled device (CCD) detector for ordinary optical image collection and digital image collection, a NIR LC imaging spectrometer for NIR chemical image wavelength selection and a room temperature or optionally
  • FIG. 1 is a schematic diagram of the NIR chemical imaging microscope.
  • NIR illumination is directed to the sample in a reflected light configuration using a QTH source or other broadband white light source, including metal halide or Xe arc lamps 1 or a transmitted light configuration using QTH or suitable NIR source 2 of an NIR optimized refractive optical microscope platform 3 .
  • the reflected or transmitted NIR light is collected from the sample positioned on the automated XYZ translational microscope stage 4 through an infinity-corrected NIR optimized microscope objective 5 .
  • Ordinary optical imagery of the sample can be obtained using a mirror or beamsplitter or prism arrangement inserted into turret 6 and collecting an image with an analog or digital color or monochrome charge-coupled device (CCD) or CMOS detector 7 .
  • CCD charge-coupled device
  • CMOS detector 7 CMOS detector 7 .
  • NIR chemical imaging mode the magnified NIR image is coupled through a NIR LC imaging spectrometer 8 and collected on a room temperature or cooled NIR focal plane array (FPA) detector 9 .
  • the FPA is typically comprised of indium gallium arsenide (InGaAs), but may be comprised of other NIR sensitive materials, including platinum silicide (PtSi), indium antimonide (InSb) or mercury cadmium telluride (HgCdTe).
  • PtSi platinum silicide
  • InSb indium antimonide
  • HgCdTe mercury cadmium telluride
  • a central processing unit 10 typically a Pentium computer, is used for NIR chemical image collection and processing.
  • the analog color CCD 7 , NIR FPA 9 , automated XYZ translational microscope stage 4 controlled via a controller 12 and NIR LC imaging spectrometer 8 (through LC imaging spectrometer controller 11 ) are operated with commercial software, such as Acquisition Manager (ChemIcon Inc.) in conjunction with ChemImage (ChemIcon Inc.).
  • a portion of the NIR light from the sample may be coupled to a remote NIR spectrometer (also not shown in schematic diagram).
  • LC imaging spectrometer may be of the following types: Lyot liquid crystal tunable filter (LCTF); Evans Split-Element LCTF; Solc LCTF; Ferroelectric LCTF; Liquid crystal Fabry Perot (LCFP); or a hybrid filter technology comprised of a combination of the above-mentioned LC filter types or the above mentioned filter types in combination with fixed bandbass and bandreject filters comprised of dielectric, rugate, holographic, color absorption, acousto-optic or polarization types.
  • LCTF Lyot liquid crystal tunable filter
  • Solc LCTF Solc LCTF
  • Ferroelectric LCTF Ferroelectric LCTF
  • LCFP Liquid crystal Fabry Perot
  • hybrid filter technology comprised of a combination of the above-mentioned LC filter types or the above mentioned filter types in combination with fixed bandbass and bandreject filters comprised of dielectric, rugate, holographic, color absorption, acousto-opti
  • One novel component of this invention is that a NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector without the use of a tube lens.
  • the microscope can be optimized for NIR operation through inherent design of objective and associated anti-reflective coatings, condenser and light source. To simultaneously provide high numerical apertures the objective should be refractive. To minimize chromatic aberration, maximize throughput and reduce cost the conventional tube lens can be eliminated, while having the NIR objective form the NIR image directly onto the NIR focal plane array (FPA) detector, typically of the InGaAs type.
  • the FPA can also be comprised of Si, SiGe, PtSi, InSb, HgCdTe, PdSi, Ge, analog vidicon types.
  • the FPA output is digitized using an analog or digital frame grabber approach.
  • An integrated parfocal analog CCD detector provides real-time sample positioning and focusing.
  • An analog video camera sensitive to visible radiation typically a color or monochrome CCD detector, but may be comprised of a CMOS type, is positioned parfocal with the NIR FPA detector to facilitate sample positioning and focusing without requiring direct viewing of the sample through conventional eyepieces.
  • the video camera output is typically digitized using a frame grabber approach.
  • the color image and the NIR image are fused using software. While the NIR and visible cameras often generate images having different contrast, the sample fields of view can be matched through a combination of optical and software manipulations. As a result, the NIR and visible images can be compared and even fused through the use of overlay techniques and correlation techniques to provide the user a near-real time view of both detector outputs on the same computer display. The comparative and integrated views of the sample can significantly enhance the understanding of sample morphology and architecture. By comparing the visible, NIR and NIR chemical images, additional useful information can be acquired about the chemical composition, structure and concentration of species in samples.
  • the NIR microscope can be used as a volumetric imaging instrument through the means of moving the sample through focus in the Z, axial dimension, collecting images in and out of focus and reconstructing a volumetric image of the sample in software.
  • volumetric chemical imaging in the NIR has been shown to be useful for failure analysis, product development and routine quality monitoring.
  • the potential also exists for performing quantitative analysis simultaneous with volumetric analysis.
  • Volumetric imaging can be performed in a non-contact mode without modifying the sample through the use of numerical confocal techniques, which require that the sample be imaged at discrete focal planes. The resulting images are processed and reconstructed and visualized.
  • Computational optical sectioning reconstruction techniques based on a variety of strategies have been demonstrated, including nearest neighbors and iterative deconvolution.
  • sample positioning combined with computation reconstruction is to employ a tube lens in the image formation path of the microscope which introduces chromatic aberration.
  • the sample can be interrogated as a function of sample depth by exercising the LC imaging spectrometer, collecting images at different wavelengths which penetrate to differing degrees into bulk materials. These wavelength dependent, depth dependent images can be reconstructed to form volumetric images of materials without requiring the sample to be moved, again through application of computational optical sectioning reconstruction algorithms.
  • the output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic cable. This allows conventional spectroscopic tools to be used to gather NIR spectra for traditional, high speed spectral analysis.
  • the spectrometers can be of the following types: fixed filter spectrometers; grating based spectrometers; Fourier Transform spectrometers; or Acousto-Optic spectrometers.
  • a novel method that is readily employed by the disclosed microscope invention is a method described as the Chemical Imaging Addition Method which involves seeding the sample with a material of known composition, structure and/or concentration and then generating the NIR image suitable for qualitative and quantitative analysis.
  • the Chemical Imaging Addition Method is a novel extension of a standard analytical chemical analysis technique, the Standard Addition Method.
  • a common practice in quantitative chemical analysis is to construct a standard calibration curve which is a plot of analytical response for a particular technique as a function of known analyte concentration. By measuring the analytical response from an unknown sample, an estimate of the analyte concentration can then be extrapolated from the calibration curve.
  • known quantities of the analyte are added to the samples and the increase in analytical response is measured.
  • the concentration of the unknown analyte can be found by plotting the analytical response from a series of standards and extrapolating the unknown concentration from the curve.
  • the x-axis is the concentration of added analyte after being mixed with the sample.
  • the x-intercept of the curve is the concentration of the unknown following dilution.
  • the Chemical Imaging Addition Method can be used for qualitative and quantitative analysis.
  • the Chemical Imaging Addition Method relies upon spatially isolating analyte standards in order to calibrate the Chemical Imaging analysis.
  • chemical imaging thousands of linearly independent, spatially-resolved spectra are collected in parallel of analytes found within complex host matrices. These spectra can then be processed to generate unique contrast intrinsic to analyte species without the use of stains, dyes, or contrast agents.
  • Various spectroscopic methods including near-infrared (NIR) absorption spectroscopy can be used to probe molecular composition and structure without being destructive to the sample.
  • NIR near-infrared
  • the Chemical Imaging Addition Method can involve several data processing steps, typically including, but not limited to:
  • GUI graphical user interface
  • the chemical imaging analysis cycle illustrates the steps needed to successfully extract information from chemical images and to tap the full potential provided by chemical imaging systems.
  • the cycle begins with the selection of sample measurement strategies and continues through to the presentation of a measurement solution.
  • the first step is the collection of images.
  • the related software must accommodate the full complement of chemical image acquisition configurations, including support of various spectroscopic techniques, the associated spectrometers and imaging detectors, and the sampling flexibility required by differing sample sizes and collection times. Ideally, even relatively disparate instrument designs can have one intuitive GUI to facilitate ease of use and ease of adoption.
  • the second step in the analysis cycle is data preprocessing.
  • preprocessing steps attempt to minimize contributions from chemical imaging instrument response that are not related to variations in the chemical composition of the imaged sample.
  • Some of the functionalities needed include: correction for detector response, including variations in detector quantum efficiency, bad detector pixels and cosmic events; variation in source illumination intensity across the sample; and gross differentiation between spectral lineshapes based on baseline fitting and subtraction.
  • tools available for preprocessing include ratiometric correction of detector pixel response; spectral operations such as Fourier filters and other spectral filters, normalization, mean centering, baseline correction, and smoothing; spatial operations such as cosmic filtering, low-pass filters, high-pass filters, and a number of other spatial filters.
  • a partial list includes: correlation techniques such as cosine correlation and Euclidean distance correlation; classification techniques such as principal components analysis, cluster analysis, discriminant analysis, and multi-way analysis; and spectral deconvolution techniques such as SIMPLISMA, linear spectral unmixing and multivariate curve resolution.
  • Quantitative analysis deals with the development of concentration map images. Just as in quantitative spectral analysis, a number of multivariate chemometric techniques can be used to build the calibration models. In applying quantitative chemical imaging, all of the challenges experienced in non-imaging spectral, analysis are present in quantitative chemical imaging, such as the selection of the calibration set and the verification of the model. However, in chemical imaging additional challenges exist, such as variations in sample thickness and the variability of multiple detector elements, to name a few. Depending on the quality of the models developed, the results can range from semiquantitative concentration maps to rigorous quantitative measurements.
  • Results obtained from preprocessing, qualitative analysis and quantitative analysis must be visualized.
  • Software tools must provide scaling, automapping, pseudo-color image representation, surface maps, volumetric representation, and multiple modes of presentation such as single image frame views, montage views, and animation of multidimensional chemical images, as well as a variety of digital image analysis algorithms for look up table (LUT) manipulation and contrast enhancement.
  • LUT look up table
  • Spatial Analysis and Chemical Image Measurement involve binarization of the high bit depth (typically 32 bits/pixel) chemical image using threshold and segmentation strategies.
  • analysis tools can examine a number of image domain features such as size, location, alignment, shape factors, domain count, domain density, and classification of domains based on any of the selected features. Results of these calculations can be used to develop key quantitative image parameters that can be used to characterize materials.
  • the final category of tools involves the automation of key steps or of the entire chemical image analysis process. For example, the detection of well defined features in an image can be completely automated and the results of these automated analyses can be tabulated based on any number of criteria (particle size, shape, chemical composition, etc). Automated chemical imaging platforms have been developed that can run for hours in an unsupervised fashion.
  • This invention incorporates a comprehensive analysis approach that allows user's to carefully plan experiments and optimize instrument parameters and should allow the maximum amount of information to be extracted from chemical images so that the user can make intelligent decisions.
  • the present invention can be used to characterize tellurium inclusion defects in cadmium zinc telluride (CdZnTe) semiconductor materials based on near infrared imaging.
  • NIR image frames can be collected rapidly and non-destructively in two and three spatial dimensions by collecting NIR image frames at multiple regions of interest throughout the wafer using an automated NIR imaging system.
  • the NIR image frames are subjected to image processing algorithms including background correction and image binarization. Particle analysis is performed on the binarized images to reveal tellurium inclusion statistics, sufficient to pass or fail wafers.
  • data visualization software is used to view the tellurium inclusions in two and three spatial dimensions.
  • the present invention has been used to automatically inspect tellurium inclusions in CdZnTe.
  • Compound semiconductors are challenging to fabricate. There are several steps along the manufacturing process in which defects can arise.
  • the chemical nature associated with semiconductor defects often plays a vital role in device performance.
  • Device fabrication and device processing defects can be difficult and time consuming to measure during manufacturing.
  • defective devices are often left undiagnosed until latter stages in the manufacturing process because of the inadequacy of the metrology tools being used. This results in low production yields and high costs which can be an impediment to growth in the semiconductor device market potential.
  • a potential solution is to develop a high throughput screening system capable of fusing multiple chemical imaging modalities into a single instrument.
  • Chemical imaging combines digital imaging and molecular spectroscopy for the chemical analysis of materials.
  • a modality of based on near-infrared (NIR) chemical imaging can be used to inspect tellurium inclusions in CdZnTe compound semiconductor materials.
  • CdZnTe is a leading material for use in room temperature X-ray detectors, ⁇ -ray radiation detectors and imaging devices. Applications for these devices include nuclear diagnostics, digital radiography, high-resolution astrophysical X-ray and ⁇ -ray imaging, industrial web gauging and nuclear nonproliferation. These devices are often decorated with microscopic and macroscopic defects limiting the yield of large-size, high-quality materials. Defects commonly found in these materials include cracks, grain boundaries, twin boundaries, pipes, precipitates and inclusions. CdZnTe wafers are often graded based on the size and number of Te inclusion defects present.
  • tellurium inclusions i.e., tellurium-rich domains in the 1-50 ⁇ m size range that originate as a result of morphological instabilities at the growth interface as tellurium-rich melt droplets are captured from the boundary layer ahead of the interface
  • the present invention can be used for automated characterization of microscale tellurium inclusions in CdZnTe based on volumetric NIR chemical imaging.
  • the system takes advantage of the fact that CdZnTe is transparent to infrared wavelengths (>850 nm).
  • IR-FPA infrared focal plane array
  • tellurium inclusions appear as dark, absorbing domains.
  • the invention images wafers in two and three spatial dimensions capturing raw infrared images at each region of interest. Images are automatically background equilibrated, binarized and processed.
  • the processed data provides particle statistical information such as inclusion counts, sizes, density, area and shape.
  • the system provides a rapid method for characterizing tellurium inclusions as small as 0.5 ⁇ m while virtually eliminating the subjectivity associated with manual inspection.
  • Tellurium-rich CdZnTe samples were produced by a commercial supplier (eV Products) for analysis. Samples containing high tellurium inclusion densities were purposely acquired to effectively demonstrate the capabilities of the automated tellurium inclusions mapping system.
  • the CdZnTe materials were grown by the Horizontal Bridgeman (HB) method and contained a nominal zinc cation loading concentration of 4% and an average etch pit density of 4 ⁇ 10 4 /cm 2 . The materials displayed a face A ⁇ 111> orientation and were polished on both sides. Sample thicknesses ranged from approximately 1 mm to 15 mm. No further sample preparation was necessary for the automated tellurium inclusion mapping analysis.
  • Volumetric maps of the tellurium inclusions in the CdZnTe samples were obtained by first placing the sample on the XYZ-translational stage of the automated mapping system. NIR image frames were then captured through the LC imaging spectrometer at a wavelength that maximized the Te precipitate contrast relative to the surrounding CdZnTe matrix in the X-Y direction at multiple regions of interest across the samples. Depth profiling was achieved by translating the sample focus under the microscope at user-defined increments. This process was then repeated in an iterative fashion until the entire wafer was characterized.
  • ChemImage was used to process the data.
  • the software For each wafer, the software generates a background-corrected grayscale image, a binarized image using the threshold value selected for each frame of the image, a montage view of the binarized image and particle statistics.
  • the particle statistics table includes information such as particle counts, particle sizes, particles densities, and a number of geometrical parameters such as particle area and particle aspect ratios.
  • FIGS. 3 and 4 respectively, show a digital macro bright-field image and a raw NIR microscopic transmittance image of a CdZnTe semiconductor material with numerous tellurium inclusions.
  • the left half of the wafer has been polished.
  • the tellurium inclusions appear as dark spots in the microscopic NIR image.
  • the raw NIR microscopic image was acquired using the automated near-infrared tellurium inclusion volumetric mapping system.
  • the automated particle analysis begins by applying a background correction preprocessing routine to the raw image frames.
  • a background correction preprocessing routine One of the biggest problems with the raw images collected is the gradually varying background across each image frame. As a result, a particle in one area of a frame may have a higher intensity value than the background of another area of that frame.
  • FIGS. 5A-5D illustrate the difficulty associated with selecting a threshold value for an image with a widely varying background.
  • regions 1 and 2 have mean intensity values of approximately 2600 and 1950, respectively. The whole of region 1 is primarily a particle whereas region 2 is primarily background with a small particle in the center.
  • FIG. 5A shows a raw NIR image frame collected from a single region of interest in a CdZnTe wafer. At wavelengths longer than approximately 850 nm, CdZnTe is transparent while tellurium inclusions remain opaque. A NIR image of the sample is light where there are no precipitates and dark where there are precipitates.
  • FIG. 5A shows a raw NIR image frame collected from a single region of interest in a CdZnTe wafer. At wavelengths longer than approximately 850 nm, CdZnTe is transparent while tellurium inclusions remain opaque. A NIR image of the sample is light where there are no precipitates and dark where there are precipitates.
  • FIG. 5A shows
  • a background correction step is used to force the background to be essentially constant across a given image frame.
  • the procedure applies a moving window across the image frame and smoothes the resulting background before subtracting it from the frame.
  • Other operations such as low pass filtering and selective removal of bad camera pixels are also applied.
  • the second step in the automated particle analysis is the selection of the threshold value resulting in the binarized image which best reflects the number and size of particles actually present in the sample being imaged.
  • a human operator would typically approach this problem by trying multiple threshold values and comparing the resulting binarized images to the actual image to see which binarized image best matches their perception of the particles in the actual image.
  • the algorithm employed by the NIR chemical imaging microscope system takes essentially the same approach.
  • a series of threshold values are used to generate binarized images. Each binarized image is submitted to a routine that finds the particles present in the image.
  • a set of particle morphology rules was developed to determine the point at which the threshold value identifies the particles consistent with results obtained by a trained human operator. This threshold value is then further refined with using derivative operations.
  • FIGS. 6A-6C show montage views of raw, background-corrected, and binarized NIR image frames, respectively, corresponding to four adjacent regions of interest from a CdZnTe wafer. A visual inspection of these images suggests that the particle analysis adequately identifies the particles in an automated fashion.
  • FIG. 7 shows a 3D volumetric view of tellurium inclusions in CdZnTe generated from 50 individual image slices.
  • FIG. 7 is constructed using a nearest neighbors computational approach for volume reconstruction. Improved results can be obtained using more sophisticated strategies that deconvolve the entire image volume using iterative deconvolution approaches.
  • the staring time of the sensor used to gather the volumetric data was less than 1 sec.
  • the total acquisition time for the data generated in this figure was well under a minute. Note how the inclusions tend to form in planes described as veils. These veils are believed to be subgrain boundaries within the CdZnTe material. Grain boundaries provide low energy nucleation sites for the inclusions to form during the growth process.
  • Table 1 provides tabulated statistical information on the volumetric data shown in FIG. 7 .
  • Defects such as tellurium inclusions affect the electrical properties in CdZnTe semiconductor materials, degrading end-product device performance. Having the ability to rapidly and non-invasively identify and quantify tellurium inclusion defects at critical stages in the fabrication process provides semiconductor manufacturers with information that will enable them to optimize the manufacturing process and reduce production costs.
  • the Automated NIR Volumetric Mapping System described here is capable of providing such information.
  • the system provides qualitative and quantitative information about tellurium inclusions present in CdZnTe wafers in two and three spatial dimensions. This system boasts improved spatial resolution ( ⁇ 0.5 ⁇ m) compared to systems currently used by many semiconductor manufacturers and it virtually eliminates the subjectivity associated with human counting and sizing measurements. Whole wafers are capable of being characterized in minutes.
  • the present invention has been demonstrated in connection with the characterization of semiconductors, it is to be expressly understood that the present invention can also be used in the characterization of other materials including, but not limited to, food and agricultural products, paper products, pharmaceutical materials, polymers, thin films and in medical uses.

Abstract

A chemical imaging system is provided which uses a near infrared radiation microscope. The system includes an illumination source which illuminates an area of a sample using light in the near infrared radiation wavelength and light in the visible wavelength. A multitude of spatially resolved spectra of transmitted, reflected, or emitted or scattered near infrared wavelength radiation light from the illuminated area of the sample is collected and a collimated beam is produced therefrom. A near infrared imaging spectrometer is provided for selecting a near infrared radiation images of the collimated beam. The spectrometer comprises a liquid crystal tunable filter. The filtered selected images are collected by a detector for further processing. The visible wavelength light from the illuminated area of the sample is simultaneously detected providing for the simultaneous visible and near infrared chemical imaging analysis of the sample. Two efficient means for performing three dimensional near infrared chemical imaging microscopy are provided.

Description

This application claims the benefit of U.S. Provisional Application No. 60/239,969, entitled “Near Infrared Chemical Imaging Microscope” filed Oct. 13, 2000.
This work is supported by the National Institute of Standards and Technology (NIST) under the Advanced Technology Program (ATP) award (Contract Number 70NANB8H4021)
FIELD OF INVENTION
The present invention is related to near-infrared (NIR) microscopes for spectroscopic and image analysis, and, in particular, to microscopes useful for both NIR spectroscopy, NIR chemical imaging and NIR volumetric chemical imaging.
BACKGROUND OF THE INVENTION
NIR spectroscopy is a mature, non-contact, non-destructive analytical characterization tool that has been widely applied to a broad range of materials. The NIR region of the electromagnetic spectrum encompasses radiation with wavelength of 0.78 to 2.5 μm (12,800 to 4,000 cm−1). NIR spectra result from the overtone and combination bands of fundamental mid-infrared (MIR) bands. Among the many desirable characteristics, NIR is used to rapidly obtain both qualitative and quantitative information about the molecular makeup of a material. Digital imaging, on the other hand, provides a means to obtain optical (i.e., spatial—morphological, topographical, etc.) information about a material. By combining the spatial information obtained from digital imagery and the spectral information obtained from NIR spectroscopy, the chemical makeup of complex material matrices can be mapped out in both two and three spatial dimensions. NIR chemical imaging combines NIR spectroscopy and digital imaging for the molecular-specific analysis of materials. A NIR chemical imaging microscope apparatus employing NIR absorption molecular spectroscopy for materials characterization is disclosed.
State-of-the-Art Instrumentation
NIR microscopes are used to obtain NIR absorption, transmittance or reflectance spectra (e.g., NIR microspectra) from samples ranging in size between 1 and 1000 μm. These instruments are typically equipped with a digital camera to visually locate a region of interest on a sample upon which a NIR light beam from a Fourier transform (FT) spectrometer is focused. Reflective optics are used to direct the transmitted or reflected light from the sample to a NIR detector. The output is a NIR absorption spectrum collected in transmittance or reflectance mode.
NIR chemical imaging can be considered an extension of NIR microspectroscopy. Much of the imaging performed since the development of the first NIR microprobes has involved spatial scanning of samples beneath NIR microscopes in order to construct NIR “maps” of surfaces. In point by point scanning with NIR microscopes, the NIR light beam is focused onto the surface of a sample or apertured to illuminate a small region of a sample and a spectrum from each spatial position is collected. Images are obtained by rastering the sample through the focused or apertured NIR light beam and the spectra recorded are then reconstructed to form an image. Although point scanning produces images based on NIR contrast, long experimental times are common since the duration of the experiment is proportional to the number of image pixels. As a direct result, point scan images are captured at low image definition, which relates directly to the limited utility of the technique as an imaging tool for the routine assessment of material morphology. The spatial resolution of the image is limited by the size of the NIR illumination spot on the sample (no less than 1 μm) and the rastering mechanism, which requires the use of moving mechanical parts that are challenging to operate reproducibly.
NIR imaging cameras have been used in photography for decades. Until recently, however, it has not been easily accessible to those not versed in traditional photographic processes. By using optical filters (e.g., cold filters) that block the visible wavelengths (0.4-0.78 μm), charge-coupled devices (CCDs) used in digital cameras and camcorders can be used to sense NIR light out to around 1100 nm. Other regions of the NIR spectrum can be viewed using devices such as indium gallium arsenide (InGaAs—0.9 μm to 1.7 μm) and indium antimonide (InSb—1.0 μm to 5.0 μm) focal plane array (FPA) detectors. These integrated wavelength NIR imaging approaches allow one to study relative light intensities of objects over broad ranges of the NIR spectrum, but useful chemical information is unattainable without the use of some type of discrete wavelength filtering device.
The use of dielectric interference filters in combination with NIR FPAs is one method in which chemical information can be obtained from a sample. To form NIR chemical images, a NIR light beam is defocused to illuminate a wide field of view and the reflected or transmitted light from the illuminated area is imaged onto a two-dimensional NIR detector. A selection of discrete dielectric interference filters provided in a filter wheel, or a linearly variable or circularly variable format can be positioned in front of a broadband NIR light source, or in front of the NIR FPA itself in order to collect NIR wavelength resolved images. Typically, the use of several fixed bandpass filters is required to access the entire NIR spectrum. The spatial resolution of the NIR image approaches that of the optical microscope, while spectral resolution of several nanometers has been demonstrated. Key limitations of the dielectric filter approach include the need for a multitude of discrete filters to provide appreciable free spectral range, or the reliance on moving mechanical parts in employing continuously tunable dielectric interference filters as a requirement to form wavelength resolved images. While moving mechanical assemblies can be engineered they add cost and complexity to NIR chemical imaging systems. Alternatives to moving mechanical assemblies are generally more cost effective and provide performance advantages.
Acoustic-optic tunable filters (AOTFs) have been employed as no-moving-parts imaging spectrometers for NIR imaging. The AOTF is a solid-state device that is capable of functioning from the UV to the mid-IR depending on the choice of the filter's crystal material. Operation of the AOTF is based on the interaction of light with a traveling acoustic sound wave in an anisotropic crystal medium. The incident light is diffracted with a narrow spectral bandpass when an rf signal is applied to the device. By changing the applied rf frequency under computer control the spectral passband can be tuned rapidly with the benefit of non-moving parts.
For use in NIR chemical imaging, AOTFs have distinct limitations. AOTFs have imaging performance that is degraded appreciably from diffraction-limited conditions due to dispersion effects and image shifting effects. Furthermore, AOTFs suffer from temperature instability and exhibit nonlinear properties that complicate their use as imaging spectrometers.
An aim of NIR chemical imaging technology development has been to develop a NIR imaging technique that combines diffraction-limited spatial resolution with high spectral resolution. NIR chemical imaging techniques have only recently achieved a degree of technological maturity that allow the collection of high resolution (spectral and spatial) data with the advent of the liquid crystal (LC) imaging spectrometers. In general, LC devices provide diffraction-limited spatial resolution. The spectral resolution of the LC imaging spectrometer is comparable to that provided by dispersive monochromator and Fourier transform interferometers. In addition, LC technology provides high out of band rejection, broad free spectral range, moderate transmittance, high overall etendue and highly reproducible random access computer controlled tuning.
Under normal NIR imaging operation, LC imaging spectrometers allow NIR chemical images of samples to be recorded at discrete wavelengths (energies). A spectrum is generated corresponding to thousands of spatial locations at the sample surface by tuning the LC imaging spectrometer over a range of wavelengths and collecting NIR images systematically. Contrast is generated in the images based on the relative amounts of NIR absorption, transmittance or reflectance that is generated by the different species located throughout the sample. Since a high quality NIR spectrum is generated for each pixel location, a wide variety of chemometric analysis tools, both univariate and multivariate, can be applied to the NIR image data to extract pertinent information. Correlative multivariate routines are particularly powerful when applied to chemical images collected from samples intentionally seeded with a known standard material. This approach of incorporating calibration standards within an image field of view can be extended to quantitative chemical image analysis. In addition, digital image analysis procedures can also be applied to high image quality NIR chemical images to perform routine particle analysis in both two (2D) and three (3D) spatial dimensions. Volumetric 3D NIR chemical image and analysis can be performed very effectively using numerical deconvolution computational strategies.
SUMMARY OF THE INVENTION
To address the need for a device that can provide video imaging, NIR spectroscopy and high resolution (spatial and spectral) NIR chemical imaging in two and three spatial dimensions, a novel NIR chemical imaging microscope has been developed that is NIR chemical imaging capable.
The microscope design uses NIR optimized liquid crystal (LC) imaging spectrometer technology for wavelength selection. The NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector with or without the use of a tube lens. An integrated parfocal analog color CCD detector provides real-time sample positioning and focusing. The color image and the NIR image are fused in software. In one configuration, the NIR microscope may be used as a volumetric imaging instrument through the means of moving the sample through focus, collecting images at varying focal depths and reconstructing a volumetric image of the sample in software, or through the means of keeping the sample fixed and changing the wavelength dependent depth of penetration in conjunction with a refractive tube lens with a well characterized chromatic effect. The output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic. A Chemical Imaging Addition Method seeds the sample with a material of known composition, structure and/or concentration and then generates the NIR image suitable for qualitative and quantitative analysis. The microscope generates NIR chemical image data that is analyzed and visualized using chemical image analysis software in a systematic and comprehensive manner. While this invention has been demonstrated on a microscope optic platform, the novel concepts are also applicable to other image gathering platforms, namely fiberscopes, macrolens systems and telescopes.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows a schematic diagram of the near-infrared (NIR)chemical imaging microscope.
FIG. 2 shows a diagram of the chemical imaging data analysis cycle performed in software.
FIG. 3 is a digital brightfield image of a CdZnTe semiconductor material decorated with tellurium inclusions.
FIG. 4 an NIR microscopic transmittance image of a CdZnTe semiconductor material decorated with tellurium inclusions.
FIG. 5A illustrates a raw NIR image frame of a CdZnTe wafer sample.
FIG. 5B illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too low.
FIG. 5C illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too high.
FIG. 5D illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set to an intermediate level.
FIG. 6A is the original raw image of four adjacent regions of interest on a CdZnTe wafer.
FIG. 6B is the background-corrected image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6A.
FIG. 6C is the binarized image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6A.
FIG. 7 is a three-dimensional view of tellurium inclusions in a CdZnTe wafer.
DETAILED DESCRIPTION OF THE INVENTION
The NIR chemical imaging microscope combines in a single platform a NIR optimized refractive optical microscope base, which is equipped with NIR optimized infinity-corrected microscope objectives, an automated XYZ translational microscope stage and quartz tungsten halogen (QTH) lamps to secure and illuminate samples for NIR spectroscopy and imaging, an analog color charge-coupled device (CCD) detector for ordinary optical image collection and digital image collection, a NIR LC imaging spectrometer for NIR chemical image wavelength selection and a room temperature or optionally cooled NIR FPA for NIR image capture.
FIG. 1 is a schematic diagram of the NIR chemical imaging microscope. NIR illumination is directed to the sample in a reflected light configuration using a QTH source or other broadband white light source, including metal halide or Xe arc lamps 1 or a transmitted light configuration using QTH or suitable NIR source 2 of an NIR optimized refractive optical microscope platform 3. The reflected or transmitted NIR light is collected from the sample positioned on the automated XYZ translational microscope stage 4 through an infinity-corrected NIR optimized microscope objective 5.
Ordinary optical imagery of the sample can be obtained using a mirror or beamsplitter or prism arrangement inserted into turret 6 and collecting an image with an analog or digital color or monochrome charge-coupled device (CCD) or CMOS detector 7. In NIR chemical imaging mode, the magnified NIR image is coupled through a NIR LC imaging spectrometer 8 and collected on a room temperature or cooled NIR focal plane array (FPA) detector 9. The FPA is typically comprised of indium gallium arsenide (InGaAs), but may be comprised of other NIR sensitive materials, including platinum silicide (PtSi), indium antimonide (InSb) or mercury cadmium telluride (HgCdTe). Using a beam-splitting element inserted into turret 6, NIR and ordinary optical imagery can be collected with an analog monochrome or color CCD detector 7 and NIR FPA 9 simultaneously.
A central processing unit 10, typically a Pentium computer, is used for NIR chemical image collection and processing. The analog color CCD 7, NIR FPA 9, automated XYZ translational microscope stage 4 controlled via a controller 12 and NIR LC imaging spectrometer 8 (through LC imaging spectrometer controller 11) are operated with commercial software, such as Acquisition Manager (ChemIcon Inc.) in conjunction with ChemImage (ChemIcon Inc.).
By introducing a polarization sensitive beam splitting element in the optical path prior to the NIR LC imaging spectrometer 8 (not shown in schematic diagram), a portion of the NIR light from the sample may be coupled to a remote NIR spectrometer (also not shown in schematic diagram).
Preferably, NIR optimized liquid crystal (LC) imaging spectrometer technology is used for wavelength selection. The LC imaging spectrometer may be of the following types: Lyot liquid crystal tunable filter (LCTF); Evans Split-Element LCTF; Solc LCTF; Ferroelectric LCTF; Liquid crystal Fabry Perot (LCFP); or a hybrid filter technology comprised of a combination of the above-mentioned LC filter types or the above mentioned filter types in combination with fixed bandbass and bandreject filters comprised of dielectric, rugate, holographic, color absorption, acousto-optic or polarization types.
One novel component of this invention, is that a NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector without the use of a tube lens. The microscope can be optimized for NIR operation through inherent design of objective and associated anti-reflective coatings, condenser and light source. To simultaneously provide high numerical apertures the objective should be refractive. To minimize chromatic aberration, maximize throughput and reduce cost the conventional tube lens can be eliminated, while having the NIR objective form the NIR image directly onto the NIR focal plane array (FPA) detector, typically of the InGaAs type. The FPA can also be comprised of Si, SiGe, PtSi, InSb, HgCdTe, PdSi, Ge, analog vidicon types. The FPA output is digitized using an analog or digital frame grabber approach.
An integrated parfocal analog CCD detector provides real-time sample positioning and focusing. An analog video camera sensitive to visible radiation, typically a color or monochrome CCD detector, but may be comprised of a CMOS type, is positioned parfocal with the NIR FPA detector to facilitate sample positioning and focusing without requiring direct viewing of the sample through conventional eyepieces. The video camera output is typically digitized using a frame grabber approach.
The color image and the NIR image are fused using software. While the NIR and visible cameras often generate images having different contrast, the sample fields of view can be matched through a combination of optical and software manipulations. As a result, the NIR and visible images can be compared and even fused through the use of overlay techniques and correlation techniques to provide the user a near-real time view of both detector outputs on the same computer display. The comparative and integrated views of the sample can significantly enhance the understanding of sample morphology and architecture. By comparing the visible, NIR and NIR chemical images, additional useful information can be acquired about the chemical composition, structure and concentration of species in samples.
The NIR microscope can be used as a volumetric imaging instrument through the means of moving the sample through focus in the Z, axial dimension, collecting images in and out of focus and reconstructing a volumetric image of the sample in software. For samples having some volume (bulk materials, surfaces, interfaces, interphases), volumetric chemical imaging in the NIR has been shown to be useful for failure analysis, product development and routine quality monitoring. The potential also exists for performing quantitative analysis simultaneous with volumetric analysis. Volumetric imaging can be performed in a non-contact mode without modifying the sample through the use of numerical confocal techniques, which require that the sample be imaged at discrete focal planes. The resulting images are processed and reconstructed and visualized. Computational optical sectioning reconstruction techniques based on a variety of strategies have been demonstrated, including nearest neighbors and iterative deconvolution.
An alternative to sample positioning combined with computation reconstruction is to employ a tube lens in the image formation path of the microscope which introduces chromatic aberration. As a result the sample can be interrogated as a function of sample depth by exercising the LC imaging spectrometer, collecting images at different wavelengths which penetrate to differing degrees into bulk materials. These wavelength dependent, depth dependent images can be reconstructed to form volumetric images of materials without requiring the sample to be moved, again through application of computational optical sectioning reconstruction algorithms.
The output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic cable. This allows conventional spectroscopic tools to be used to gather NIR spectra for traditional, high speed spectral analysis. The spectrometers can be of the following types: fixed filter spectrometers; grating based spectrometers; Fourier Transform spectrometers; or Acousto-Optic spectrometers.
A novel method that is readily employed by the disclosed microscope invention is a method described as the Chemical Imaging Addition Method which involves seeding the sample with a material of known composition, structure and/or concentration and then generating the NIR image suitable for qualitative and quantitative analysis. The Chemical Imaging Addition Method is a novel extension of a standard analytical chemical analysis technique, the Standard Addition Method. A common practice in quantitative chemical analysis is to construct a standard calibration curve which is a plot of analytical response for a particular technique as a function of known analyte concentration. By measuring the analytical response from an unknown sample, an estimate of the analyte concentration can then be extrapolated from the calibration curve. In the Standard Addition Method, known quantities of the analyte are added to the samples and the increase in analytical response is measured. When the analytical response is linearly related to concentration, the concentration of the unknown analyte can be found by plotting the analytical response from a series of standards and extrapolating the unknown concentration from the curve. In this graph, however, the x-axis is the concentration of added analyte after being mixed with the sample. The x-intercept of the curve is the concentration of the unknown following dilution. The primary advantage of the standard addition method is that the matrix remains constant for all samples.
While the Standard Addition Method is used specifically for quantitative analysis, the Chemical Imaging Addition Method can be used for qualitative and quantitative analysis. The Chemical Imaging Addition Method relies upon spatially isolating analyte standards in order to calibrate the Chemical Imaging analysis. In chemical imaging, thousands of linearly independent, spatially-resolved spectra are collected in parallel of analytes found within complex host matrices. These spectra can then be processed to generate unique contrast intrinsic to analyte species without the use of stains, dyes, or contrast agents. Various spectroscopic methods including near-infrared (NIR) absorption spectroscopy can be used to probe molecular composition and structure without being destructive to the sample. Similarly, in NIR chemical imaging the contrast that is generated reveals the spatial distribution of properties revealed in the underlying NIR spectra.
The Chemical Imaging Addition Method can involve several data processing steps, typically including, but not limited to:
    • 1. Ratiometric correction in which the sample NIR image is divided by the background NIR image to produce a result having a floating point data type.
    • 2. The divided image is normalized by dividing each intensity value at every pixel in the image by the vector norm for its corresponding pixel spectrum. Where the vector norm is the square root of the sum of the squares of pixel intensity values for each pixel spectrum. Normalization is applied for qualitative analysis of NIR chemical images. For quantitative analysis, normalization is not employed, but relies instead on the use of partial least squares regression (PLSR) techniques.
    • 3. Correlation analysis, including Euclidian Distance and Cosine correlation analysis (CCA) are established multivariate image analysis techniques that assess similarity in spectral image data while simultaneously suppressing background effects. More specifically, CCA assesses chemical heterogeneity without the need for training sets, identifies differences in spectral shape and efficiently provides chemical image based contrast that is independent of absolute intensity. The CCA algorithm treats each pixel spectrum as a projected vector in n-dimensional space, where n is the number of wavelengths sampled in the image. An orthonormal basis set of vectors is chosen as the set of reference vectors and the cosine of the angles between each pixel spectrum vector and the reference vectors are calculated. The intensity values displayed in the resulting CCA images are these cosine values, where a cosine value of 1 indicates the pixel spectrum and reference spectrum are identical, and a cosine value of 0 indicates the pixel spectrum and the reference spectrum are orthogonal (no correlation). The dimensions of the resulting CCA image is the same as the original image because the orthonormal basis set provides n reference vectors, resulting in n CCA images.
    • 4. Principal component analysis (PCA) is a data space dimensionally reduction technique. A least squares fit is drawn through the maximum variance in the n-dimensional dataset. The vector resulting from this least squares fit is termed the first principal component (PC) or the first loading. After subtracting the variance explained from the first PC, the operation is repeated and the second principal component is calculated. This process is repeated until some percentage of the total variance in the data space is explained (normally 95% or greater). PC Score images can then be visualized to reveal orthogonal information including sample information, as well as instrument response, including noise. Reconstruction of spectral dimension data can then be performed guided by cluster analysis, including without PCs that describe material or instrument parameters that one desires to amplify or suppress, depending on the needs of the sensing application.
Effective materials characterization with the disclosed NIR chemical imaging microscope invention typically requires application of a multitude of software procedures to the NIR chemical image. A schematic of the chemical image analysis cycle is shown in FIG. 2. A fairly comprehensive description of the variety of steps used to process chemical images is described below.
Until recently, seamless integration of spectral analysis, chemometric analysis and digital image analysis has not been commercially available. Individual communities have independently developed advanced software applicable to their specific requirements. For example, digital imaging software packages that treat single-frame gray-scale images and spectral processing programs that apply chemometric techniques have both reached a relatively mature state. One limitation to the development of chemical imaging, however, has been the lack of integrated software that combines enough of the features of each of these individual disciplines to have practical utility.
Historically, practitioners of chemical imaging were forced to develop their own software routines to perform each of the key steps of the data analysis. Typically, routines were prototyped using packages that supported scripting capability, such as Matlab, IDL, Grams or LabView. These packages, while flexible, are limited by steep learning curves, computational inefficiencies, and the need for individual practitioners to develop their own graphical user interface (GUI). Today, commercially available software does exist that provides efficient data processing and the ease of use of a simple GUI.
Software that meets these goals must address the entirety of the chemical imaging process. The chemical imaging analysis cycle illustrates the steps needed to successfully extract information from chemical images and to tap the full potential provided by chemical imaging systems. The cycle begins with the selection of sample measurement strategies and continues through to the presentation of a measurement solution. The first step is the collection of images. The related software must accommodate the full complement of chemical image acquisition configurations, including support of various spectroscopic techniques, the associated spectrometers and imaging detectors, and the sampling flexibility required by differing sample sizes and collection times. Ideally, even relatively disparate instrument designs can have one intuitive GUI to facilitate ease of use and ease of adoption.
The second step in the analysis cycle is data preprocessing. In general, preprocessing steps attempt to minimize contributions from chemical imaging instrument response that are not related to variations in the chemical composition of the imaged sample. Some of the functionalities needed include: correction for detector response, including variations in detector quantum efficiency, bad detector pixels and cosmic events; variation in source illumination intensity across the sample; and gross differentiation between spectral lineshapes based on baseline fitting and subtraction. Examples of tools available for preprocessing include ratiometric correction of detector pixel response; spectral operations such as Fourier filters and other spectral filters, normalization, mean centering, baseline correction, and smoothing; spatial operations such as cosmic filtering, low-pass filters, high-pass filters, and a number of other spatial filters.
Once instrument response has been suppressed, qualitative processing can be employed. Qualitative chemical image analysis attempts to address a simple question, “What is present and how is it distributed?”. Many chemometric tools fall under this category. A partial list includes: correlation techniques such as cosine correlation and Euclidean distance correlation; classification techniques such as principal components analysis, cluster analysis, discriminant analysis, and multi-way analysis; and spectral deconvolution techniques such as SIMPLISMA, linear spectral unmixing and multivariate curve resolution.
Quantitative analysis deals with the development of concentration map images. Just as in quantitative spectral analysis, a number of multivariate chemometric techniques can be used to build the calibration models. In applying quantitative chemical imaging, all of the challenges experienced in non-imaging spectral, analysis are present in quantitative chemical imaging, such as the selection of the calibration set and the verification of the model. However, in chemical imaging additional challenges exist, such as variations in sample thickness and the variability of multiple detector elements, to name a few. Depending on the quality of the models developed, the results can range from semiquantitative concentration maps to rigorous quantitative measurements.
Results obtained from preprocessing, qualitative analysis and quantitative analysis must be visualized. Software tools must provide scaling, automapping, pseudo-color image representation, surface maps, volumetric representation, and multiple modes of presentation such as single image frame views, montage views, and animation of multidimensional chemical images, as well as a variety of digital image analysis algorithms for look up table (LUT) manipulation and contrast enhancement.
Once digital chemical images have been generated, traditional digital image analysis can be applied. For example, Spatial Analysis and Chemical Image Measurement involve binarization of the high bit depth (typically 32 bits/pixel) chemical image using threshold and segmentation strategies. Once binary images have been generated, analysis tools can examine a number of image domain features such as size, location, alignment, shape factors, domain count, domain density, and classification of domains based on any of the selected features. Results of these calculations can be used to develop key quantitative image parameters that can be used to characterize materials.
The final category of tools, Automated Image Processing, involves the automation of key steps or of the entire chemical image analysis process. For example, the detection of well defined features in an image can be completely automated and the results of these automated analyses can be tabulated based on any number of criteria (particle size, shape, chemical composition, etc). Automated chemical imaging platforms have been developed that can run for hours in an unsupervised fashion.
This invention incorporates a comprehensive analysis approach that allows user's to carefully plan experiments and optimize instrument parameters and should allow the maximum amount of information to be extracted from chemical images so that the user can make intelligent decisions.
EXAMPLE
Overview
As the demand for high quality, low cost X-ray, γ-ray and imaging detector devices increases, there is a need to improve the quality and production yield of semiconductor materials used in these devices. One effective strategy for improving semiconductor device yield is through the use of better device characterization tools that can rapidly and nondestructively identify defects at early stages in the fabrication process. Early screening helps to elucidate the underlying causes of defects and to reduce downstream costs associated with processing defect laden materials that are ultimately scrapped. The present invention can be used to characterize tellurium inclusion defects in cadmium zinc telluride (CdZnTe) semiconductor materials based on near infrared imaging. With this approach, large area wafers can be inspected rapidly and non-destructively in two and three spatial dimensions by collecting NIR image frames at multiple regions of interest throughout the wafer using an automated NIR imaging system. The NIR image frames are subjected to image processing algorithms including background correction and image binarization. Particle analysis is performed on the binarized images to reveal tellurium inclusion statistics, sufficient to pass or fail wafers. In addition, data visualization software is used to view the tellurium inclusions in two and three spatial dimensions.
Background
The present invention has been used to automatically inspect tellurium inclusions in CdZnTe. Compound semiconductors are challenging to fabricate. There are several steps along the manufacturing process in which defects can arise. The chemical nature associated with semiconductor defects often plays a vital role in device performance. Device fabrication and device processing defects can be difficult and time consuming to measure during manufacturing. Unfortunately, defective devices are often left undiagnosed until latter stages in the manufacturing process because of the inadequacy of the metrology tools being used. This results in low production yields and high costs which can be an impediment to growth in the semiconductor device market potential.
There is a general need in the semiconductor industry for metrology technologies that can nondestructively assess semiconductor material defects and ultimately increase manufacturing yields. A potential solution is to develop a high throughput screening system capable of fusing multiple chemical imaging modalities into a single instrument. Chemical imaging combines digital imaging and molecular spectroscopy for the chemical analysis of materials. A modality of based on near-infrared (NIR) chemical imaging can be used to inspect tellurium inclusions in CdZnTe compound semiconductor materials.
CdZnTe is a leading material for use in room temperature X-ray detectors, γ-ray radiation detectors and imaging devices. Applications for these devices include nuclear diagnostics, digital radiography, high-resolution astrophysical X-ray and γ-ray imaging, industrial web gauging and nuclear nonproliferation. These devices are often decorated with microscopic and macroscopic defects limiting the yield of large-size, high-quality materials. Defects commonly found in these materials include cracks, grain boundaries, twin boundaries, pipes, precipitates and inclusions. CdZnTe wafers are often graded based on the size and number of Te inclusion defects present.
The definition used by Rudolph and Muhlberg for tellurium inclusions (i.e., tellurium-rich domains in the 1-50 μm size range that originate as a result of morphological instabilities at the growth interface as tellurium-rich melt droplets are captured from the boundary layer ahead of the interface) has been adopted and is used herein. There have been numerous studies on the composition and distribution of tellurium inclusions in CdZnTe material. It has been demonstrated that the presence of tellurium inclusions can impair the electronic properties of CdZnTe materials—consequently degrading the end-product device performance.
The current procedure used by low volume semiconductor manufacturers for characterizing tellurium inclusions in CdZnTe is labor intensive, susceptible to human error and provides little information on inclusions in the 1-5 μm size scale. Inclusions are viewed and counted manually by a human operator using an IR microscope platform. When an inclusion is identified that is suspected to exceed a specified size limit, a Polaroid film photograph is taken. An overlay of a stage micrometer is laid over the photograph to determine the size. This analysis is relatively time consuming, often taking several minutes to characterize a region of interest from a large wafer.
The present invention can be used for automated characterization of microscale tellurium inclusions in CdZnTe based on volumetric NIR chemical imaging. The system takes advantage of the fact that CdZnTe is transparent to infrared wavelengths (>850 nm). When viewing CdZnTe with an infrared focal plane array (IR-FPA) through a NIR LC imaging spectrometer, tellurium inclusions appear as dark, absorbing domains. The invention images wafers in two and three spatial dimensions capturing raw infrared images at each region of interest. Images are automatically background equilibrated, binarized and processed. The processed data provides particle statistical information such as inclusion counts, sizes, density, area and shape. The system provides a rapid method for characterizing tellurium inclusions as small as 0.5 μm while virtually eliminating the subjectivity associated with manual inspection.
Sample Description
Tellurium-rich CdZnTe samples were produced by a commercial supplier (eV Products) for analysis. Samples containing high tellurium inclusion densities were purposely acquired to effectively demonstrate the capabilities of the automated tellurium inclusions mapping system. The CdZnTe materials were grown by the Horizontal Bridgeman (HB) method and contained a nominal zinc cation loading concentration of 4% and an average etch pit density of 4×104/cm2. The materials displayed a face A <111> orientation and were polished on both sides. Sample thicknesses ranged from approximately 1 mm to 15 mm. No further sample preparation was necessary for the automated tellurium inclusion mapping analysis.
Data Collection
Volumetric maps of the tellurium inclusions in the CdZnTe samples were obtained by first placing the sample on the XYZ-translational stage of the automated mapping system. NIR image frames were then captured through the LC imaging spectrometer at a wavelength that maximized the Te precipitate contrast relative to the surrounding CdZnTe matrix in the X-Y direction at multiple regions of interest across the samples. Depth profiling was achieved by translating the sample focus under the microscope at user-defined increments. This process was then repeated in an iterative fashion until the entire wafer was characterized.
Data Processing
Once imaging data was collected, ChemImage was used to process the data. For each wafer, the software generates a background-corrected grayscale image, a binarized image using the threshold value selected for each frame of the image, a montage view of the binarized image and particle statistics. The particle statistics table includes information such as particle counts, particle sizes, particles densities, and a number of geometrical parameters such as particle area and particle aspect ratios.
NIR Imaging
FIGS. 3 and 4, respectively, show a digital macro bright-field image and a raw NIR microscopic transmittance image of a CdZnTe semiconductor material with numerous tellurium inclusions. The left half of the wafer has been polished. The tellurium inclusions appear as dark spots in the microscopic NIR image. The raw NIR microscopic image was acquired using the automated near-infrared tellurium inclusion volumetric mapping system.
Background Correction and Image Binarization
The automated particle analysis begins by applying a background correction preprocessing routine to the raw image frames. One of the biggest problems with the raw images collected is the gradually varying background across each image frame. As a result, a particle in one area of a frame may have a higher intensity value than the background of another area of that frame.
FIGS. 5A-5D illustrate the difficulty associated with selecting a threshold value for an image with a widely varying background. In FIGS. 5A-5D, regions 1 and 2 have mean intensity values of approximately 2600 and 1950, respectively. The whole of region 1 is primarily a particle whereas region 2 is primarily background with a small particle in the center. FIG. 5A shows a raw NIR image frame collected from a single region of interest in a CdZnTe wafer. At wavelengths longer than approximately 850 nm, CdZnTe is transparent while tellurium inclusions remain opaque. A NIR image of the sample is light where there are no precipitates and dark where there are precipitates. In FIG. 5B, the threshold value is set low enough (value=1520) that the particle in region 2 is correctly identified, but most of the remaining particles are not found. In FIG. 5C, the threshold value is set high enough (value=2470) so that all particles are detected. Unfortunately, a large area of the frame is incorrectly identified as one very large particle. FIG. 5D displays the case in which the threshold is set to an intermediate value (value=1960). Many of the particles are correctly identified, but the particle in region 2 is identified as being larger than it actually is.
To address this issue, a background correction step is used to force the background to be essentially constant across a given image frame. The procedure applies a moving window across the image frame and smoothes the resulting background before subtracting it from the frame. Other operations such as low pass filtering and selective removal of bad camera pixels are also applied.
The second step in the automated particle analysis is the selection of the threshold value resulting in the binarized image which best reflects the number and size of particles actually present in the sample being imaged. A human operator would typically approach this problem by trying multiple threshold values and comparing the resulting binarized images to the actual image to see which binarized image best matches their perception of the particles in the actual image. The algorithm employed by the NIR chemical imaging microscope system takes essentially the same approach. A series of threshold values are used to generate binarized images. Each binarized image is submitted to a routine that finds the particles present in the image. A set of particle morphology rules was developed to determine the point at which the threshold value identifies the particles consistent with results obtained by a trained human operator. This threshold value is then further refined with using derivative operations.
FIGS. 6A-6C show montage views of raw, background-corrected, and binarized NIR image frames, respectively, corresponding to four adjacent regions of interest from a CdZnTe wafer. A visual inspection of these images suggests that the particle analysis adequately identifies the particles in an automated fashion.
Volumetric Reconstruction and Visualization
It is of particular interest to the semiconductor manufacturing industry to view defects, including tellurium inclusions in this example, in a three dimensional volumetric view. Individual binarized image frames generated at discrete axial planes of focus have been reconstructed into a volumetric view allowing users to view tellurium inclusions in three-dimensional space.
FIG. 7 shows a 3D volumetric view of tellurium inclusions in CdZnTe generated from 50 individual image slices. FIG. 7 is constructed using a nearest neighbors computational approach for volume reconstruction. Improved results can be obtained using more sophisticated strategies that deconvolve the entire image volume using iterative deconvolution approaches. The staring time of the sensor used to gather the volumetric data was less than 1 sec. The total acquisition time for the data generated in this figure was well under a minute. Note how the inclusions tend to form in planes described as veils. These veils are believed to be subgrain boundaries within the CdZnTe material. Grain boundaries provide low energy nucleation sites for the inclusions to form during the growth process.
Table 1 provides tabulated statistical information on the volumetric data shown in FIG. 7.
TABLE 1
Particle Statistics
Slice Number and Depth (μm)
Parameters 0 (0) 10 (89.77) 20 (189.52) 30 (289.26) 40 (389.01) 50 (488.75)
# of Inclusions 25 30 27 24 25 36
Mean Diameter (μm) 12.12 11.38 12.75 15.70 12.89 13.73
Density (Inclusions/cm2) 4368 5241 4717 4193 4368 6289
Area (μm2) 97.48 73.78 91.67 119.25 96.29 98.15
Perimeter (μm) 40.40 37.32 43.27 50.72 41.93 43.98
Shape Factor 0.60 0.60 0.58 0.53 0.60 0.55
Maximum Chord Length 12.12 11.38 12.75 15.70 12.89 13.73
Feret 1 Diameter 9.17 9.56 11.33 12.64 10.48 10.16
Feret 2 Diameter 10.26 9.01 10.10 12.18 10.37 11.60
Aspect Ratio 1.02 1.19 1.16 1.08 1.02 0.95
Defects such as tellurium inclusions affect the electrical properties in CdZnTe semiconductor materials, degrading end-product device performance. Having the ability to rapidly and non-invasively identify and quantify tellurium inclusion defects at critical stages in the fabrication process provides semiconductor manufacturers with information that will enable them to optimize the manufacturing process and reduce production costs. The Automated NIR Volumetric Mapping System described here is capable of providing such information. The system provides qualitative and quantitative information about tellurium inclusions present in CdZnTe wafers in two and three spatial dimensions. This system boasts improved spatial resolution (−0.5 μm) compared to systems currently used by many semiconductor manufacturers and it virtually eliminates the subjectivity associated with human counting and sizing measurements. Whole wafers are capable of being characterized in minutes.
While in the above example, the present invention has been demonstrated in connection with the characterization of semiconductors, it is to be expressly understood that the present invention can also be used in the characterization of other materials including, but not limited to, food and agricultural products, paper products, pharmaceutical materials, polymers, thin films and in medical uses.
Although present preferred embodiments of the invention have been shown and described, it should be distinctly understood that the invention is not limited thereto but may be variously embodied within the scope of the following claims.

Claims (16)

1. A near infrared radiation chemical imaging system comprising:
a) an illumination source for illuminating an area of a sample using light in the near infrared radiation wavelength;
b) a device for collecting a spectrum of near infrared wavelength radiation light transmitted, reflected, emitted or scattered from said illuminated area of said sample and producing a collimated beam therefrom;
c) a near infrared imaging spectrometer for selecting a near infrared radiation image of said collimated beam; and
d) a detector for collecting said filtered near infrared images.
2. The system of claim 1 wherein said illumination source is one of a quartz tungsten halogen lamp, a tunable laser, a metal halide lamp, and a xenon arc lamp.
3. The system of claim 1 wherein said device for collecting is one of a refractive type infinity-corrected near infrared optimized microscope objective, a refractive fixed tube length microscope objective, and a reflecting microscope objective.
4. The system of claim 1 wherein said near infrared imaging spectrometer is selected from the group consisting of Lyot liquid crystal tunable filters; Evans Split-Element liquid crystal tunable filters; Solc liquid crystal tunable filters; Ferroelectric liquid crystal tunable filters; Liquid crystal Fabry Perot filters; a hybrid filter formed from a combination of liquid crystal tunable filters; and a combination of a liquid crystal tunable filter and a fixed bandpass and bandreject filters.
5. The system of claim 1 wherein said detector is a near infrared radiation focal plane array detector.
6. The system of claim 5 wherein said detector is selected from the group consisting of indium gallium arsenide, platinum silicide, indium antimonide, palladium silicide, indium germanide, and mercury cadmium telluride.
7. The system of claim 1 A near infrared radiation chemical imaging system comprising:
a) an illumination source for illuminating an area of a sample using light in the near infrared radiation wavelength;
b) a device for collecting a spectrum of near infrared wavelength radiation light transmitted, reflected or emitted from said illuminated area of said sample and producing a collimated beam therefrom;
c) a near infrared imaging spectrometer for selecting near infrared radiation images of said collimated beam, wherein the spectrometer comprises a liquid crystal tunable filter; and
d) a detector for collecting said selected near infrared images;
further comprising a visible wavelength imagery system.
8. The system of claim 7 wherein said visible imagery system comprises:
a) an illumination source for illuminating an area of said sample using light in the visible optical wavelengths; and
b) a device for detecting said visible wavelength light from said illuminated area of said sample.
9. The system of claim 8 wherein said device for detecting said visible wavelength light comprises an analog and digital detector based on at least one of a silicon charge-coupled device detector and a silicon CMOS detectors.
10. The system of claim 8 further comprising a processor for producing a near infrared radiation chemical image of said sample.
11. The system of claim 8 further comprising an algorithm for combining the near infrared and visible image data.
12. A chemical imaging system comprising:
a) an illumination source for illuminating an area of a sample using light in the near infrared radiation wavelength and light in the visible wavelength;
b) a device for collecting a spectrum of near infrared wavelength radiation light transmitted, reflected, or emitted or scattered from said illuminated area of said sample and producing a collimated beam therefrom;
c) a near infrared imaging spectrometer for selecting a near infrared radiation images of said collimated beam, wherein the spectrometer comprises a liquid crystal tunable filer;
d) detector for collecting said filtered selected near infrared images; and
e) a device for detecting said visible wavelength light from said illuminated area of said sample.
13. A chemical imaging method comprising the steps of:
a) illuminating an area of a sample using light in the near infrared radiation wavelength and light in the visible wavelength;
b) collecting a spectrum of near infrared wavelength radiation light transmitted, reflected, or emitted or scattered from said illuminated area of said sample and producing a collimated beam therefore;
c) filtering said collimated beam to produce a near infrared radiation images of said collimated beam while simultaneously detecting said optical visible wavelength light from said illuminated area of said sample, wherein the filtering is performed using a liquid crystal tunable filter;
d) collecting said filtered near infrared images; and
e) processing said collected near infrared images to produce and display a chemical image of said sample.
14. A method for producing a volumetric image of a sample comprising the steps of: a) incorporating a refractive image formation optic exhibiting a chromatic response in the optical path of the microscope before the near infrared detector; b) collecting images of said sample at a plurality of near infrared wavelengths through said objective at a fixed focus condition; and c) processing said collected images to reconstruct and display a depth resolved image of said sample.
15. A method for chemically analyzing a sample comprising the steps of: a) seeding said sample with a plurality of analytes having at least one of a known composition, structure and concentration; b) collecting a plurality of spatially-resolved spectra for said plurality of analytes; c) producing a plurality of chemical images of said sample containing said plurality of analytes; and d) processing said plurality of chemical images to generate a chemical image of said sample, and displaying said chemical image.
16. The method of claim 15 wherein said processing step comprises at least one of:
a) correcting the image by dividing a near infrared image of said sample by a near infrared image of a background of said image to produce a resulting ratioed image;
b) normalizing the divided image by dividing each intensity value at every pixel in the image by the vector norm for its corresponding pixel spectrum, said vector norm being the square root of the sum of the squares of pixel intensity values for each pixel spectrum;
c) processing said image using a cosine correlation analysis method wherein each pixel spectrum is treated as a projected vector in n-dimensional space, wherein n is the number of wavelengths sampled in the image; and
d) processing said image using a principal component analysis method wherein a least squares fit is drawn through the maximum variance in the n-dimensional dataset.
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060124443A1 (en) * 2004-11-03 2006-06-15 David Tuschel Control and monitoring of non-resonant radiation-induced nucleation, crystallization, and polymorph formation
US20100225899A1 (en) * 2005-12-23 2010-09-09 Chemimage Corporation Chemical Imaging Explosives (CHIMED) Optical Sensor using SWIR
US20110080577A1 (en) * 2006-06-09 2011-04-07 Chemlmage Corporation System and Method for Combined Raman, SWIR and LIBS Detection
US20110089323A1 (en) * 2009-10-06 2011-04-21 Chemlmage Corporation System and methods for explosives detection using SWIR
US20110237446A1 (en) * 2006-06-09 2011-09-29 Chemlmage Corporation Detection of Pathogenic Microorganisms Using Fused Raman, SWIR and LIBS Sensor Data
US8054454B2 (en) 2005-07-14 2011-11-08 Chemimage Corporation Time and space resolved standoff hyperspectral IED explosives LIDAR detector
US20120147224A1 (en) * 2010-12-08 2012-06-14 Canon Kabushiki Kaisha Imaging apparatus
US8729502B1 (en) 2010-10-28 2014-05-20 The Research Foundation For The State University Of New York Simultaneous, single-detector fluorescence detection of multiple analytes with frequency-specific lock-in detection
US11684249B2 (en) * 2011-07-07 2023-06-27 Boston Scientific Scimed, Inc. Imaging system for endoscope

Families Citing this family (113)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6734962B2 (en) * 2000-10-13 2004-05-11 Chemimage Corporation Near infrared chemical imaging microscope
GB0026173D0 (en) * 2000-10-26 2000-12-13 Imerys Minerals Ltd Processing of inorganic particulate materials
US7155049B2 (en) * 2001-01-11 2006-12-26 Trestle Acquisition Corp. System for creating microscopic digital montage images
US20120062873A1 (en) * 2001-06-28 2012-03-15 Chemimage Corporation System and method for diagnosing the disease state of breast tissue using swir
US6965793B2 (en) * 2001-06-28 2005-11-15 Chemimage Corporation Method for Raman chemical imaging of endogenous chemicals to reveal tissue lesion boundaries in tissue
US8078268B2 (en) 2001-06-28 2011-12-13 Chemimage Corporation System and method of chemical imaging using pulsed laser excitation and time-gated detection to determine tissue margins during surgery
US20070192035A1 (en) * 2005-06-09 2007-08-16 Chem Image Corporation Forensic integrated search technology
JP2005515423A (en) * 2002-01-10 2005-05-26 ケムルメイジ コーポレーション Method for detecting pathogenic microorganisms
US8395769B2 (en) * 2002-01-10 2013-03-12 Chemimage Corporation Method for analysis of pathogenic microorganisms using raman spectroscopic techniques
US20040184660A1 (en) * 2002-10-31 2004-09-23 Treado Patrick J. Method for improved forensic analysis
JP3944574B2 (en) * 2003-02-14 2007-07-11 国立大学法人東京海洋大学 Weight measuring device
US20060082762A1 (en) * 2003-03-26 2006-04-20 Chad Leverette Automated polarized light microscope combined with a spectroscopy/spectral imaging apparatus
US7057177B2 (en) * 2003-04-29 2006-06-06 The Boeing Company Infrared imaging for evaluation of corrosion test coupons
US7119336B2 (en) * 2003-06-20 2006-10-10 The Boeing Company Method of measuring coating using two-wavelength infrared reflectance
US7542138B2 (en) * 2003-07-18 2009-06-02 Chemimage Corporation Sample container and system for a handheld spectrometer and method for using therefor
US7538869B2 (en) 2004-06-30 2009-05-26 Chemimage Corporation Multipoint method for identifying hazardous agents
US7286222B2 (en) * 2003-07-18 2007-10-23 Chemimage Corporation Sample container and system for a handheld spectrometer and method for using therefor
US7115869B2 (en) * 2003-09-30 2006-10-03 The Boeing Company Method for measurement of composite heat damage with infrared spectroscopy
CA2445426A1 (en) * 2003-10-17 2005-04-17 Alberta Research Council Inc. A method for characterizing a dispersion using transformation techniques
JP4354262B2 (en) * 2003-12-08 2009-10-28 株式会社ディスコ Confirmation method of laser-processed altered layer
US7217913B2 (en) * 2003-12-18 2007-05-15 Micron Technology, Inc. Method and system for wavelength-dependent imaging and detection using a hybrid filter
DE102004029212B4 (en) * 2004-06-16 2006-07-13 Leica Microsystems Semiconductor Gmbh Apparatus and method for optical inspection and / or transmitted light inspection of microstructures in the IR
US7454050B2 (en) * 2004-06-18 2008-11-18 Csi Technology, Inc. Method of automating a thermographic inspection process
US7532320B2 (en) * 2004-06-30 2009-05-12 Chemimage Corporation Multimodal method for identifying hazardous agents
US7218822B2 (en) 2004-09-03 2007-05-15 Chemimage Corporation Method and apparatus for fiberscope
US7525654B2 (en) * 2004-10-20 2009-04-28 Duquesne University Of The Holy Spirit Tunable laser-based chemical imaging system
US7728873B2 (en) * 2004-11-09 2010-06-01 Cnoga Ltd Apparatus for obtaining and electronically interpreting digital images of liquids, solids and combinations on liquids and solids
US7528950B2 (en) * 2005-01-11 2009-05-05 Duquesne University Of The Holy Spirit Tunable laser-based process monitoring apparatus
US7362489B2 (en) * 2005-02-02 2008-04-22 Chemimage Corporation Multi-conjugate liquid crystal tunable filter
US7495752B2 (en) * 2005-02-09 2009-02-24 Chemimage Corporation System and method for the deposition, detection and identification of threat agents
US7709796B2 (en) * 2005-02-25 2010-05-04 Iscon Video Imaging, Inc. Methods and systems for detecting presence of materials
US7283232B2 (en) * 2005-06-06 2007-10-16 Duke University Optical spectroscopy with overlapping images
US20090012723A1 (en) * 2005-06-09 2009-01-08 Chemlmage Corporation Adaptive Method for Outlier Detection and Spectral Library Augmentation
EP1904824A1 (en) * 2005-07-14 2008-04-02 Battelle Memorial Institute Aerosol trigger device and methods of detecting particulates of interest using and aerosol trigger device
EP1904826B1 (en) * 2005-07-14 2019-02-20 Battelle Memorial Institute Systems and methods for biological and chemical detection
WO2007038410A2 (en) * 2005-09-27 2007-04-05 Chemimage Corporation Liquid crystal filter with tunable rejection band
US8415624B2 (en) * 2005-10-06 2013-04-09 Polestar Technologies, Inc. Differential wavelength imaging method and system for detection and identification of concealed materials
US7561274B2 (en) * 2005-10-20 2009-07-14 Duke University Optical spectroscopy utilizing planar spectral filters
US7684039B2 (en) * 2005-11-18 2010-03-23 Kla-Tencor Technologies Corporation Overlay metrology using the near infra-red spectral range
US20070145258A1 (en) * 2005-12-16 2007-06-28 Nelson Matthew P Method and apparatus for automated spectral calibration
US7859753B2 (en) * 2005-12-21 2010-12-28 Chem Image Corporation Optical birefringence filters with interleaved absorptive and zero degree reflective polarizers
US7848000B2 (en) * 2006-01-09 2010-12-07 Chemimage Corporation Birefringent spectral filter with wide field of view and associated communications method and apparatus
US7379181B2 (en) * 2006-01-19 2008-05-27 Centice Corporation Structured coded aperture fiber bundles
EP1977205A4 (en) 2006-01-23 2010-06-09 Chemimage Corp Method and system for combined raman and libs detection
WO2008039758A2 (en) * 2006-09-25 2008-04-03 Cambridge Research & Instrumentation, Inc. Sample imaging and classification
US7417796B2 (en) * 2006-09-29 2008-08-26 Chemimage Corporation Wavelength discrimination filter for infrared wavelengths
WO2008037068A1 (en) * 2006-09-29 2008-04-03 Ottawa Health Research Institute Correlation technique for analysis of clinical condition
US7840360B1 (en) 2006-10-26 2010-11-23 Micheels Ronald H Optical system and method for inspection and characterization of liquids in vessels
WO2008085914A1 (en) 2007-01-05 2008-07-17 Malvern Instruments, Inc. Spectrometric investigation of heterogeneity
US20120134582A1 (en) * 2007-01-16 2012-05-31 Chemimage Corporation System and Method for Multimodal Detection of Unknown Substances Including Explosives
US7478008B2 (en) * 2007-03-16 2009-01-13 Cordis Corporation System and method for the non-destructive assessment of the quantitative spatial distribution of components of a medical device
US8156568B2 (en) * 2007-04-27 2012-04-10 Picocal, Inc. Hybrid contact mode scanning cantilever system
US7952719B2 (en) * 2007-06-08 2011-05-31 Prescient Medical, Inc. Optical catheter configurations combining raman spectroscopy with optical fiber-based low coherence reflectometry
WO2009014820A1 (en) * 2007-07-20 2009-01-29 Prescient Medical, Inc. Wall-contacting intravascular ultrasound probe catheters
US9713448B2 (en) 2008-04-03 2017-07-25 Infraredx, Inc. System and method for intravascular structural analysis compensation of chemical analysis modality
WO2013109966A1 (en) * 2012-01-20 2013-07-25 The Trustees Of Dartmouth College Method and apparatus for quantitative hyperspectral fluorescence and reflectance imaging for surgical guidance
US10568535B2 (en) 2008-05-22 2020-02-25 The Trustees Of Dartmouth College Surgical navigation with stereovision and associated methods
US7919753B2 (en) * 2008-06-28 2011-04-05 The Boeing Company Method for performing IR spectroscopy measurements to quantify a level of UV effect
US7897923B2 (en) * 2008-06-28 2011-03-01 The Boeing Company Sample preparation and methods for portable IR spectroscopy measurements of UV and thermal effect
US8519337B2 (en) * 2008-06-28 2013-08-27 The Boeing Company Thermal effect measurement with near-infrared spectroscopy
US8552382B2 (en) * 2008-08-14 2013-10-08 The Boeing Company Thermal effect measurement with mid-infrared spectroscopy
JP2012506060A (en) * 2008-10-14 2012-03-08 サンフォード−バーナム メディカル リサーチ インスティテュート Automated scanning cytometry using chromatic aberration for multi-plane image acquisition.
CN102265124A (en) 2008-11-04 2011-11-30 威廉马什赖斯大学 Image mapping spectrometers
US20100113906A1 (en) * 2008-11-06 2010-05-06 Prescient Medical, Inc. Hybrid basket catheters
US8644547B2 (en) * 2008-11-14 2014-02-04 The Scripps Research Institute Image analysis platform for identifying artifacts in samples and laboratory consumables
US8440959B2 (en) * 2008-11-18 2013-05-14 Chemimage Corporation Method and apparatus for automated spectral calibration
US8130904B2 (en) 2009-01-29 2012-03-06 The Invention Science Fund I, Llc Diagnostic delivery service
US8111809B2 (en) 2009-01-29 2012-02-07 The Invention Science Fund I, Llc Diagnostic delivery service
US8290301B2 (en) * 2009-02-06 2012-10-16 Raytheon Company Optimized imaging system for collection of high resolution imagery
US8520970B2 (en) 2010-04-23 2013-08-27 Flir Systems Ab Infrared resolution and contrast enhancement with fusion
US8320637B2 (en) * 2009-08-04 2012-11-27 Chemimage Corporation System and method for hyperspectral imaging of treated fingerprints
US8593630B2 (en) * 2009-10-07 2013-11-26 The Board Of Trustees Of The University Of Illinois Discrete frequency spectroscopy and instrumentation
US8823802B2 (en) * 2009-10-15 2014-09-02 University Of South Carolina Multi-mode imaging in the thermal infrared for chemical contrast enhancement
US8492721B2 (en) * 2009-10-15 2013-07-23 Camtek Ltd. Systems and methods for near infra-red optical inspection
WO2011146093A2 (en) 2009-12-15 2011-11-24 William Marsh Rice University Electricity generation
US9291506B2 (en) * 2010-01-27 2016-03-22 Ci Systems Ltd. Room-temperature filtering for passive infrared imaging
US9121760B2 (en) * 2010-01-27 2015-09-01 Ci Systems Ltd. Room-temperature filtering for passive infrared imaging
JP2013524217A (en) * 2010-03-29 2013-06-17 インテヴァック インコーポレイテッド Time-resolved photoluminescence imaging system and photovoltaic cell inspection method
US8462981B2 (en) * 2010-04-07 2013-06-11 Cambridge Research & Instrumentation, Inc. Spectral unmixing for visualization of samples
US8400574B2 (en) 2010-04-16 2013-03-19 Chemimage Corporation Short wave infrared multi-conjugate liquid crystal tunable filter
US8563934B2 (en) * 2010-09-10 2013-10-22 Mississippi State University Method and detection system for detection of aflatoxin in corn with fluorescence spectra
WO2012051394A1 (en) * 2010-10-14 2012-04-19 The Arizona Board Of Regents On Behalf Of The University Of Arizona Methods and apparatus for imaging detecting, and monitoring surficial and subdermal inflammation
US9863662B2 (en) 2010-12-15 2018-01-09 William Marsh Rice University Generating a heated fluid using an electromagnetic radiation-absorbing complex
US9222665B2 (en) 2010-12-15 2015-12-29 William Marsh Rice University Waste remediation
US8736777B2 (en) 2011-01-19 2014-05-27 Chemimage Technologies Llc VIS-SNIR multi-conjugate liquid crystal tunable filter
US9256013B2 (en) 2011-04-14 2016-02-09 Chemimage Technologies Llc Short-wavelength infrared (SWIR) multi-conjugate liquid crystal tunable filter
WO2012159205A1 (en) * 2011-05-25 2012-11-29 Huron Technologies International Inc. 3d pathology slide scanner
US10001622B2 (en) 2011-10-25 2018-06-19 Sanford Burnham Medical Research Institute Multifunction autofocus system and method for automated microscopy
US9305237B2 (en) * 2011-11-04 2016-04-05 Polestar Technologies, Inc. Methods and systems for detection and identification of concealed materials
WO2013067329A1 (en) * 2011-11-04 2013-05-10 Polestar Technologies, Inc. Methods and systems for detection and identification of concealed materials
US11510600B2 (en) 2012-01-04 2022-11-29 The Trustees Of Dartmouth College Method and apparatus for quantitative and depth resolved hyperspectral fluorescence and reflectance imaging for surgical guidance
CN102854166B (en) * 2012-06-19 2014-10-08 中国农业大学 Identifying and locating method of melamine in plant protein feedstuff
US10509976B2 (en) 2012-06-22 2019-12-17 Malvern Panalytical Limited Heterogeneous fluid sample characterization
ITTO20120763A1 (en) 2012-09-05 2014-03-06 Consiglio Nazionale Ricerche PROCEDURE AND SYSTEM FOR THE THREE-DIMENSIONAL RECONSTRUCTION OF FORMATIONS MISSING IN A MATERIAL MATRIX, IN PARTICULAR INCLUSIONS IN CRYSTALLINE MATRICES
IL291122B2 (en) * 2013-04-23 2024-01-01 Cedars Sinai Medical Center Systems and methods for recording simultaneously visible light image and infrared light image from fluorophores
US9407838B2 (en) 2013-04-23 2016-08-02 Cedars-Sinai Medical Center Systems and methods for recording simultaneously visible light image and infrared light image from fluorophores
US9665689B2 (en) * 2013-05-17 2017-05-30 Viavi Solutions Inc. Medication assurance system and method
BR112016009907B1 (en) 2013-11-11 2021-07-06 Halliburton Energy Services, Inc measurement method and system
WO2015125134A1 (en) * 2014-02-21 2015-08-27 Gem Solar Ltd. A method and apparatus for internal marking of ingots and wafers
US11300773B2 (en) * 2014-09-29 2022-04-12 Agilent Technologies, Inc. Mid-infrared scanning system
US10261298B1 (en) 2014-12-09 2019-04-16 The Board Of Trustees Of The Leland Stanford Junior University Near-infrared-II confocal microscope and methods of use
US10041866B2 (en) 2015-04-24 2018-08-07 University Of South Carolina Reproducible sample preparation method for quantitative stain detection
US9885147B2 (en) 2015-04-24 2018-02-06 University Of South Carolina Reproducible sample preparation method for quantitative stain detection
CN105158169A (en) * 2015-06-03 2015-12-16 遵义师范学院 Camellia seed component content software detection system and method
US9924115B2 (en) * 2015-09-23 2018-03-20 Agilent Technologies, Inc. Apparatus and method for three-dimensional infrared imaging of surfaces
JP7015101B2 (en) * 2016-03-16 2022-02-02 東亜ディーケーケー株式会社 Analytical method and liquid electrode plasma emission spectrometer
US10378006B2 (en) * 2017-04-19 2019-08-13 The Florida International University Board Of Trustees Near-infrared ray exposure system for biological studies
US20210075978A1 (en) * 2017-09-15 2021-03-11 Kent Imaging Hybrid Visible and Near Infrared Imaging with an RGB Color Filter Array Sensor
US11564349B2 (en) 2018-10-31 2023-01-31 Deere & Company Controlling a machine based on cracked kernel detection
AU2020259802A1 (en) * 2019-04-17 2021-11-18 Swinburne University Of Technology A system and method for asbestos identification
CN110672550B (en) * 2019-09-10 2021-11-19 中国科学院上海技术物理研究所 Image spectrum analyzer for important biological resources in micro-area
US20230119953A1 (en) * 2020-03-24 2023-04-20 The Regents Of The University Of California Infrared chemical imaging through nondegenerate two-photon absorption in silicon-based cameras
CN112881467B (en) * 2021-03-15 2023-04-28 中国空气动力研究与发展中心超高速空气动力研究所 Large-size composite material damage imaging and quantitative identification method

Citations (50)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3806257A (en) 1973-05-04 1974-04-23 Corning Glass Works Operator viewing optics for a slide classification system
EP0174778B1 (en) 1984-08-30 1988-11-30 Rank Cintel Limited Apparatus for individually processing optical images of different wavelengths
US4802760A (en) 1987-03-25 1989-02-07 Mitsubishi Denki Kabushiki Kaisha Raman microprobe apparatus for determining crystal orientation
US5194912A (en) 1988-12-22 1993-03-16 Renishaw Plc Raman analysis apparatus
US5377004A (en) 1993-10-15 1994-12-27 Kaiser Optical Systems Remote optical measurement probe
US5377003A (en) 1992-03-06 1994-12-27 The United States Of America As Represented By The Department Of Health And Human Services Spectroscopic imaging device employing imaging quality spectral filters
US5381236A (en) 1991-02-12 1995-01-10 Oxford Sensor Technology Limited Optical sensor for imaging an object
US5394499A (en) 1992-12-28 1995-02-28 Olympus Optical Co., Ltd. Observation system with an endoscope
WO1995011624A2 (en) 1993-10-29 1995-05-04 Feld Michael S A raman endoscope
US5442438A (en) 1988-12-22 1995-08-15 Renishaw Plc Spectroscopic apparatus and methods
US5493443A (en) 1992-12-19 1996-02-20 Bruker Analytisch Mebtechnik Gmbh Lens for a FT-raman microscope
US5528393A (en) 1989-10-30 1996-06-18 Regents Of The University Of Colorado Split-element liquid crystal tunable optical filter
US5623342A (en) 1995-06-07 1997-04-22 Renishaw Plc. Raman microscope
US5626134A (en) 1994-04-21 1997-05-06 Zuckerman; Ralph Method and apparatus for the measurement of analyte concentration levels by the steady-state determination of fluorescence lifetime
US5645550A (en) 1994-04-08 1997-07-08 Chiron Technolas Gmbh Ophthalmologische System Method and apparatus for providing precise location of points on the eye
US5668664A (en) 1995-06-14 1997-09-16 Asahi Seimitsu Kabushiki Kaisha Color separation prism assembly for C-mount camera
US5710626A (en) 1996-11-15 1998-01-20 Westinghouse Savannah River Company Rugged fiber optic probe for raman measurement
WO1998035262A1 (en) 1997-02-06 1998-08-13 Morphometrix Technologies Inc. Infrared spectroscopy for medical imaging
US5862273A (en) 1996-02-23 1999-01-19 Kaiser Optical Systems, Inc. Fiber optic probe with integral optical filtering
US5866430A (en) 1996-06-13 1999-02-02 Grow; Ann E. Raman optrode processes and devices for detection of chemicals and microorganisms
US5900975A (en) 1997-10-30 1999-05-04 Cognex Corporation Ghost image extinction in an active range sensor
US5901261A (en) 1997-06-19 1999-05-04 Visionex, Inc. Fiber optic interface for optical probes with enhanced photonic efficiency, light manipulation, and stray light rejection
US5910816A (en) 1995-06-07 1999-06-08 Stryker Corporation Imaging system with independent processing of visible an infrared light energy
US5911017A (en) 1996-07-31 1999-06-08 Visionex, Inc. Fiber optic interface for laser spectroscopic Raman probes
US5943129A (en) * 1997-08-07 1999-08-24 Cambridge Research & Instrumentation Inc. Fluorescence imaging system
US5943122A (en) 1998-07-10 1999-08-24 Nanometrics Incorporated Integrated optical measurement instruments
US5974211A (en) 1997-02-07 1999-10-26 Kaiser Optical Systems Enhanced collection efficiency fiber-optic probe
US6002476A (en) 1998-04-22 1999-12-14 Chemicon Inc. Chemical imaging system
US6006001A (en) 1996-12-02 1999-12-21 The Research Foundation Of Cuny Fiberoptic assembly useful in optical spectroscopy
US6052187A (en) 1998-08-31 2000-04-18 Containerless Research, Inc. Hyperspectral polarization profiler for remote sensing
US6069690A (en) 1998-11-13 2000-05-30 Uniphase Corporation Integrated laser imaging and spectral analysis system
US6088100A (en) 1997-07-14 2000-07-11 Massachusetts Institute Of Technology Three-dimensional light absorption spectroscopic imaging
US6091872A (en) 1996-10-29 2000-07-18 Katoot; Mohammad W. Optical fiber imaging system
US6175750B1 (en) 1999-03-19 2001-01-16 Cytometrics, Inc. System and method for calibrating a reflection imaging spectrophotometer
US6181414B1 (en) 1998-02-06 2001-01-30 Morphometrix Technologies Inc Infrared spectroscopy for medical imaging
US6222970B1 (en) 1995-11-20 2001-04-24 Cirrex Corp. Methods and apparatus for filtering an optical fiber
US6274871B1 (en) 1998-10-22 2001-08-14 Vysis, Inc. Method and system for performing infrared study on a biological sample
US6300618B1 (en) 1997-12-12 2001-10-09 Yokogawa Electric Corporation High speed 3-dimensional confocal microscopic equipment
US6392752B1 (en) 1999-06-14 2002-05-21 Kenneth Carlisle Johnson Phase-measuring microlens microscopy
US6403947B1 (en) * 1999-03-18 2002-06-11 Cambridge Research & Instrumentation Inc. High-efficiency multiple probe imaging system
US6404497B1 (en) 1999-01-25 2002-06-11 Massachusetts Institute Of Technology Polarized light scattering spectroscopy of tissue
US6483641B1 (en) 1997-10-29 2002-11-19 Digital Optical Imaging Corporation Apparatus and methods relating to spatially light modulated microscopy
US6485413B1 (en) 1991-04-29 2002-11-26 The General Hospital Corporation Methods and apparatus for forward-directed optical scanning instruments
US6530882B1 (en) 2000-06-30 2003-03-11 Inner Vision Imaging, L.L.C. Endoscope having microscopic and macroscopic magnification
US6571117B1 (en) 2000-08-11 2003-05-27 Ralf Marbach Capillary sweet spot imaging for improving the tracking accuracy and SNR of noninvasive blood analysis methods
US6614532B1 (en) 2000-04-28 2003-09-02 Mcgill University Apparatus and method for light profile microscopy
US6640130B1 (en) 1999-07-02 2003-10-28 Hypermed, Inc. Integrated imaging apparatus
US20040004715A1 (en) 1999-07-19 2004-01-08 David Tuschel Method for Raman imaging of semiconductor materials
US6697665B1 (en) 1991-02-26 2004-02-24 Massachusetts Institute Of Technology Systems and methods of molecular spectroscopy to provide for the diagnosis of tissue
US6711283B1 (en) * 2000-05-03 2004-03-23 Aperio Technologies, Inc. Fully automatic rapid microscope slide scanner

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE3303140A1 (en) * 1983-01-31 1984-08-02 Bruker Analytische Meßtechnik GmbH, 7512 Rheinstetten INFRARED SPECTROMETER
FR2666382B1 (en) * 1990-08-28 1992-10-16 Cit Alcatel DEVICE FOR PUMPING A GAS BY AN OIL SEAL PUMP AND APPLICATION TO HELIUM LEAK DETECTORS.
JPH1096691A (en) * 1991-03-19 1998-04-14 Tokai Rika Co Ltd Method and apparatus for analyzing plane
USRE36529E (en) * 1992-03-06 2000-01-25 The United States Of America As Represented By The Department Of Health And Human Services Spectroscopic imaging device employing imaging quality spectral filters
US5452723A (en) * 1992-07-24 1995-09-26 Massachusetts Institute Of Technology Calibrated spectrographic imaging
EP0767361B1 (en) * 1993-07-22 2000-02-23 Applied Spectral Imaging Ltd. Method and apparatus for spectral imaging
US6690817B1 (en) * 1993-08-18 2004-02-10 Applied Spectral Imaging Ltd. Spectral bio-imaging data for cell classification using internal reference
US5532873A (en) * 1993-09-08 1996-07-02 Dixon; Arthur E. Scanning beam laser microscope with wide range of magnification
US6232602B1 (en) * 1999-03-05 2001-05-15 Flir Systems, Inc. Enhanced vision system sensitive to infrared radiation
US6548796B1 (en) * 1999-06-23 2003-04-15 Regents Of The University Of Minnesota Confocal macroscope
US6734962B2 (en) * 2000-10-13 2004-05-11 Chemimage Corporation Near infrared chemical imaging microscope
EP1208367A4 (en) * 1999-08-06 2007-03-07 Cambridge Res & Instrmnt Inc Spectral imaging system
US6392572B1 (en) * 2001-05-11 2002-05-21 Qualcomm Incorporated Buffer architecture for a turbo decoder

Patent Citations (51)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3806257A (en) 1973-05-04 1974-04-23 Corning Glass Works Operator viewing optics for a slide classification system
EP0174778B1 (en) 1984-08-30 1988-11-30 Rank Cintel Limited Apparatus for individually processing optical images of different wavelengths
US4802760A (en) 1987-03-25 1989-02-07 Mitsubishi Denki Kabushiki Kaisha Raman microprobe apparatus for determining crystal orientation
US5194912A (en) 1988-12-22 1993-03-16 Renishaw Plc Raman analysis apparatus
US5442438A (en) 1988-12-22 1995-08-15 Renishaw Plc Spectroscopic apparatus and methods
US5689333A (en) 1988-12-22 1997-11-18 Renishaw Plc Spectroscopic apparatus and methods
US5528393A (en) 1989-10-30 1996-06-18 Regents Of The University Of Colorado Split-element liquid crystal tunable optical filter
US5381236A (en) 1991-02-12 1995-01-10 Oxford Sensor Technology Limited Optical sensor for imaging an object
US6697665B1 (en) 1991-02-26 2004-02-24 Massachusetts Institute Of Technology Systems and methods of molecular spectroscopy to provide for the diagnosis of tissue
US6485413B1 (en) 1991-04-29 2002-11-26 The General Hospital Corporation Methods and apparatus for forward-directed optical scanning instruments
US5377003A (en) 1992-03-06 1994-12-27 The United States Of America As Represented By The Department Of Health And Human Services Spectroscopic imaging device employing imaging quality spectral filters
US5493443A (en) 1992-12-19 1996-02-20 Bruker Analytisch Mebtechnik Gmbh Lens for a FT-raman microscope
US5394499A (en) 1992-12-28 1995-02-28 Olympus Optical Co., Ltd. Observation system with an endoscope
US5377004A (en) 1993-10-15 1994-12-27 Kaiser Optical Systems Remote optical measurement probe
WO1995011624A2 (en) 1993-10-29 1995-05-04 Feld Michael S A raman endoscope
US5645550A (en) 1994-04-08 1997-07-08 Chiron Technolas Gmbh Ophthalmologische System Method and apparatus for providing precise location of points on the eye
US5626134A (en) 1994-04-21 1997-05-06 Zuckerman; Ralph Method and apparatus for the measurement of analyte concentration levels by the steady-state determination of fluorescence lifetime
US5623342A (en) 1995-06-07 1997-04-22 Renishaw Plc. Raman microscope
US5910816A (en) 1995-06-07 1999-06-08 Stryker Corporation Imaging system with independent processing of visible an infrared light energy
US5668664A (en) 1995-06-14 1997-09-16 Asahi Seimitsu Kabushiki Kaisha Color separation prism assembly for C-mount camera
US6222970B1 (en) 1995-11-20 2001-04-24 Cirrex Corp. Methods and apparatus for filtering an optical fiber
US5862273A (en) 1996-02-23 1999-01-19 Kaiser Optical Systems, Inc. Fiber optic probe with integral optical filtering
US5866430A (en) 1996-06-13 1999-02-02 Grow; Ann E. Raman optrode processes and devices for detection of chemicals and microorganisms
US5911017A (en) 1996-07-31 1999-06-08 Visionex, Inc. Fiber optic interface for laser spectroscopic Raman probes
US6091872A (en) 1996-10-29 2000-07-18 Katoot; Mohammad W. Optical fiber imaging system
US5710626A (en) 1996-11-15 1998-01-20 Westinghouse Savannah River Company Rugged fiber optic probe for raman measurement
US6006001A (en) 1996-12-02 1999-12-21 The Research Foundation Of Cuny Fiberoptic assembly useful in optical spectroscopy
WO1998035262A1 (en) 1997-02-06 1998-08-13 Morphometrix Technologies Inc. Infrared spectroscopy for medical imaging
US5974211A (en) 1997-02-07 1999-10-26 Kaiser Optical Systems Enhanced collection efficiency fiber-optic probe
US5901261A (en) 1997-06-19 1999-05-04 Visionex, Inc. Fiber optic interface for optical probes with enhanced photonic efficiency, light manipulation, and stray light rejection
US6088100A (en) 1997-07-14 2000-07-11 Massachusetts Institute Of Technology Three-dimensional light absorption spectroscopic imaging
US5943129A (en) * 1997-08-07 1999-08-24 Cambridge Research & Instrumentation Inc. Fluorescence imaging system
US6483641B1 (en) 1997-10-29 2002-11-19 Digital Optical Imaging Corporation Apparatus and methods relating to spatially light modulated microscopy
US5900975A (en) 1997-10-30 1999-05-04 Cognex Corporation Ghost image extinction in an active range sensor
US6300618B1 (en) 1997-12-12 2001-10-09 Yokogawa Electric Corporation High speed 3-dimensional confocal microscopic equipment
US6181414B1 (en) 1998-02-06 2001-01-30 Morphometrix Technologies Inc Infrared spectroscopy for medical imaging
US6002476A (en) 1998-04-22 1999-12-14 Chemicon Inc. Chemical imaging system
US5943122A (en) 1998-07-10 1999-08-24 Nanometrics Incorporated Integrated optical measurement instruments
US6052187A (en) 1998-08-31 2000-04-18 Containerless Research, Inc. Hyperspectral polarization profiler for remote sensing
US6274871B1 (en) 1998-10-22 2001-08-14 Vysis, Inc. Method and system for performing infrared study on a biological sample
US6069690A (en) 1998-11-13 2000-05-30 Uniphase Corporation Integrated laser imaging and spectral analysis system
US6404497B1 (en) 1999-01-25 2002-06-11 Massachusetts Institute Of Technology Polarized light scattering spectroscopy of tissue
US6403947B1 (en) * 1999-03-18 2002-06-11 Cambridge Research & Instrumentation Inc. High-efficiency multiple probe imaging system
US6175750B1 (en) 1999-03-19 2001-01-16 Cytometrics, Inc. System and method for calibrating a reflection imaging spectrophotometer
US6392752B1 (en) 1999-06-14 2002-05-21 Kenneth Carlisle Johnson Phase-measuring microlens microscopy
US6640130B1 (en) 1999-07-02 2003-10-28 Hypermed, Inc. Integrated imaging apparatus
US20040004715A1 (en) 1999-07-19 2004-01-08 David Tuschel Method for Raman imaging of semiconductor materials
US6614532B1 (en) 2000-04-28 2003-09-02 Mcgill University Apparatus and method for light profile microscopy
US6711283B1 (en) * 2000-05-03 2004-03-23 Aperio Technologies, Inc. Fully automatic rapid microscope slide scanner
US6530882B1 (en) 2000-06-30 2003-03-11 Inner Vision Imaging, L.L.C. Endoscope having microscopic and macroscopic magnification
US6571117B1 (en) 2000-08-11 2003-05-27 Ralf Marbach Capillary sweet spot imaging for improving the tracking accuracy and SNR of noninvasive blood analysis methods

Non-Patent Citations (62)

* Cited by examiner, † Cited by third party
Title
"RAVEN Raman Imaging Fiberscope-A Tool for Insitu Material Analysis," Ben Franklin Technology Center of Western Pennsylvania, date unknown.
Christensen et al., "Raman Imaging Using a Tunable Dual-Stage Liquid Crystal Fabry-Perot Interferometer," Applied Spectroscopy, vol. 49, No. 8 (1995) pp. 1120-1125.
Christofides et al., "Reconstruction Mechanisms in Ion Implanted and Annealed Silicon Wafers," Defect and Diffusion Forum, vols. 117-118 (1985) pp. 45-64.
Dewilton et al., "A Raman Study of the Dopan Distribution in Submicron Pn Junctions in B+ or BF2+ Ion Implanted Silicon," SPIE vol. 623 Advanced Processing and Characterization of Semiconductors III (1986) pp. 26-34.
Dewilton et al., "Raman Spectroscopy For NonDestructive Depth Profile Studies Of Ion Implantation In Silicon", J. Electrochem. Soc.: Solid State Science And Technology, May 1986, pp. 988-993.
Dey et al., "Raman Scattering Characterization of Si(100) Implanted With Mega-Electron-Volt Sb", Journal of Applied Physics 87(3) Feb. 1, 2000, pp. 1110-1116.
Gerald C. Holst, "Electro-Optical Imaging System Performance," SPIE Optical Engineering Press, pp. 218-219, 238-239, 248-257,266-269, no date available.
Gift et al., "Near-Infrared Raman Imaging Microscope Based on Fiber-Bundle Image Compression," Journal of Raman Spectroscopy, vol. 30 (1999), pp. 757-765.
Gonzalez et al., "Digital Image Processing," Addison-Wesley Publishing Co. (1992) pp. 21-79.
Gorelick, "Raman and Non-Linear Light Scattering From Undersurface Layers Of Ion Implanted Silicon Crystals," Materials Science Forum, vol. 173-174 (1995) pp. 237-242.
H. Morris, C. Hoyt, P. Miller and P. Treado, "Liquid Crystal Tunalbe Filter Raman Chemical Imaging", vol. 50 Applied Spectroscopy, No. 6, pp. 805-811 (1996).
H. Skinner, T. Cooney , S. Sharma and S. Angel. "Remote Raman Microimaging Using an AOTF and a Spatially Coherent Microfiber Optical Probe". vol. 50 Applied Spectroscopy No. 8, pp. 1007-1014 (1996).
H.R. Morris and P.J. Treado, "LCTF Ramon Chemical Imaging of Thermoplastic Otefin (TPO) Architecture," Proc. Xvth ICORS, S.A. Asher, Ed (Willey, Chichester, 1996) 1186-1187.
I. Lewis and P. Griffiths, "Raman Spectrometry with Fiber-Optic Sampling", vol. 50 Applied Spectroscopy, No. 10, pp. 12A-29A (1996).
I. Lewis and P. Griffiths, "Raman Spectrometry with Fiber-Optic Sampling," Applied Spectroscopy, vol. 50, No. 10, (1996) pp. 12A-29A.
Ishioka et al., "Reduction in Raman Intensity of Si (1 1 1) Due to Defect Formation During Ion Irradiation," Solid State Communications, vol. 96, No. 6 (1995) pp. 387-390.
Jain et al., "Raman Scattering From Ion-Implanted Silicon," Physical Review B., vol. 32, No. 10, Nov. 15, 1985, pp. 6688-6691.
Jeff Hecht, "Imaging and Illuminating Fiber Optics," Chapter 28, (3rd ed. 1999) pp. 567-581.
John F. Turner ll and Patrick J. Treado, LCTF Raman Chemical Imaging in the Near-Infrared, Proc. SPIE 3061, (1997) 280-283.
Kirilov et al., "Amorphous Phase Transformation During Rapid Thermal Annealing of Ion-Implanted Si," Mat'l Res. Soc. Symp. Proc., vol. 52 (1986) pp. 131-138.
Lewis et al., "A Miniaturzed, No-Moving-Parts Raman Spectrometer," Applied Spectroscopy, vol. 47, No. 5, (1993) pp. 539-543.
M.D. Schaeberle and P.J. Treado, "LCTF Raman Chemical Imaging of Semiconductors," Proc. Xvth ICORS, S.A. Asher, Ed (Wiley, Chichester, 1996) 1188-1189.
Miller, Peter J. and Hoyt, Clifford C., "Multispectral Imaging with a Liquid Crystal Tunable Filter," SPIE vol. 2345, (1995) pp. 354-365.
Mizoguchi et al., "Raman Image Study of Flash-Lamp Annealing of Ion-Implanted Silicon," Journal of Applied Physics 77 (7) Apr. 1, 1995, pp. 3388-3392.
Morris et al., "Imaging Spectrometers for Fluorescence and Raman Microscopy: Acousto-Optic and Liquid Crystal Tunable Filter," Applied Spectroscopy, vol. 48, No. 7 (1994) pp. 857-866.
Morris, "Ultrasensitive Raman and Flourescence Imaging Using Liquid Crystal Tunable Filters," SPIE vol. 2385, (1995) pp. 81-87.
Morris, Hoyt, Miller and Treado, "Liquid Crystal Tunable Filter Raman Chemical Imaging," Applied Spectroscopy, No. 50, No. 6, Jun. 1996 pp. 805-811.
Nakashima et al., "Raman Microproble Study of Recrystallization in Ion-Implanted and Laser-Annealed Polycrystalline Silicon," Journal of Applied Physics 54(5) May 1983 pp. 2611-2617.
Othonos et al., "Multi-Wavelength Raman Probing of Phosphorus Implanted Silicon Wafers," Nucl. Instr. and Meth. in Phys. Rev. B., 117 (1996) pp. 367-374.
Othonos et al., "Multi-Wavelength Raman Probing of Phosphorus Implanted Silicon Wafers," Nuclear Instruments and Methods in Physics Research B., 117 (1996) pp. 367-374.
Othonos et al., "Raman Spectroscopy and Spreading Resistance Analysis of Phosphorous Implanted and Annealed Silicon," Journal of Applied Physics, 75 (12) Jun. 1994, pp. 8032-8038.
P. Treado et al., "High-Fidelity Raman Imaging Spectrometry: A Rapid Method Using an Acousto-Optic Tunable Filter". vol. 46 Applied Spectroscopy, No. 8, pp. 1211-(1992).
Patrick J. Treado and Michael D. Morris, Infrared and Raman Spectroscopic Imaging (Marcell Decker, New York, 1992) pp. 71-108.
Patrick J. Treado, "A Miniaturized Raman Fiberscope for Remote Chemical Imaging," Proposal Submitted to the Ben Franklin Technology Center of Western Pennsylvania, Mar. 21, 1, no date available.
Patrick J. Treado, "A Raman Endoscope for Breast Cancer Diagnosis," Proposal submitted to the Ben Franklin Technology Center of Western Pennsylvania, Apr. 8, 1998.
Patrick J. Treado, "Chemical Imaging Reveals More Than The Microscope," Laser Focus World, Oct., (1995) pp. 1-7.
Patrick J. Treado, Ira W. Levin and E. Neil Lewis, Near-Infrared Acousto-Optic Filtered Spectroscopic Microscopy: A Solid-State Approach to Chemical Imaging, Applied Spectroscopy 46, (1992) 553-559.
Patrick J. Treado, Ira W. Levin, and E. Neil Lewis, Indium Antimonide (InSb) Focal Plane Array (FPA) Detection for Near-Infrared Imaging Microscopy. Applied Spectroscopu 48. (1994) 607.
Patrick J. Treado, Letter to Peter Miller, Oct. 28, 1996.
Patrick J. Treado, Letter to Peter V. Foukal, Ph.D., Oct. 24, 1996.
Peter J. Miller, National Science Foundation SBIR Phase II Proposal, "High-Definition Raman Imaging Microscope," Oct. 1996.
Peter J. Miller, Small Business Innovation Research (SBIR) Phase I, "SBIR Phase I: High Definition Raman Imaging Microscope," Sep. 13, 1996.
Peter Miller, National Science Foundation Small Business Innovation Research Program, "High Definition Raman Imaging Microscope," Jun. 1994.
Robert D. Guenther, "Modern Optics, Polarization By BiRefringence," (John Wiley & Son 1990) pp. 529-534.
Schaeberle et al., "Multiplexed Acousto-Optic Tunable Filter (AOTF) Spectral Imaging Microscopy," SPIE, vol. 2173, (1994) pp. 11-20.
Schaeberle, et al., "Raman Chemical Imaging: Histopathology of Inclusions in Human Breast Tissue," Analytical Chemistry, vol. 68, No. 11, (1996), pp. 1829-1833.
Shukla et al., "A Raman Scattering From Ultraheavily-Iron Implanted and Laser-Annealed Silicon," Physical Review B. Vol. 34, No. 12, Dec. 15, 1986, pp. 8950-8953.
Skinner et al. "Remote Raman Microimaging Using An AOTF and a Spartially Coherent Microfiber Optical Probe," Applied Spectroscopy, vol. 50 No. 8, (1996) pp. 1007-1014.
Spectral Dimensions. NIR Systems Product Information, http://www.spectraldimensions.com/products/b-nir.html.
Treado et al, "Incorporation of a Band Pass Filter into a High Temperature Raman Illumination Fiberscope," Feb. 15, 1999.
Treado et al. "A Thousand Ponts of Light: The Hadamard Transform" Analytical Chemistry 61 (1989) Jun. 1, No. 11, pp 723-724.
Treado et al., "A Thousand Points Of Light: The Hadamard Transform In Chemical Analysis And Instrumentation," Analytical Chemistry, vol. 61, No. 11, Jun. 1, 1989, pp. 723-734.
Treado et al., "Engineering Study of the Feasibility of Incorporating a Raman Notch Filter into the Higher Temperature (HT) Fiberscope," Jul. 29, 1997.
Treado et al., "High-Fidelity Raman Imaging Spectrometry: A Rapid Method Using An Acousto-Optic Tunable Filter", Applied Spectroscopy, vol. 46, No. 8, (1992) pp. 1211-1216.
Treado et al., "Incorporation of a Notch Filter into A High Temperature Raman Collection Fiberscope," Jul. 20, 1998.
Treado et al., "Indium Antimonide (InSb) Focal Plane Array(FPA) Detection for Near-Infrared Imaging Microscopy," Applied Spectroscopy 48. (1994) p. 607.
Treado et al., "Indium Antimonide (InSb) Focal Plane Array(FPA) Detection for Near-Infrared Imaging Microscopy," Applied Spectroscopy vol. 48, No. 5. (1994) p. 607.
Treado et al., "Infrared Raman Spectroscopic Imaging," (Marcell Decker, 1992) pp. 71-108.
Treado et al., "Near-Infrared Acousto-Optic Filtered Spectroscopic Microscopy: A Solid-State Approach To Chemical Imaging", Applied Spectroscopy, vol. 46, No. 4, (1992) pp. 553-559.
Turner et al., "LCTF Raman Chemical Imaging in the Near-Infrared," SPIE vol. 3061 (1997) pp. 280-283.
Turner et al., "Near-Infrared Acousto-Optic Tunable Filter Hadamard Transform Spectroscopy," Applied Spectroscopy, vol. 50, No. 2, (1996) pp. 227-284.
Zhang et al., "Details of Damage Profile in Self-Ion-Implanted Silicon," vol. 25 Journal of Raman Spectroscopy, (1994) pp. 515-520.

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060124443A1 (en) * 2004-11-03 2006-06-15 David Tuschel Control and monitoring of non-resonant radiation-induced nucleation, crystallization, and polymorph formation
US8054454B2 (en) 2005-07-14 2011-11-08 Chemimage Corporation Time and space resolved standoff hyperspectral IED explosives LIDAR detector
US20100225899A1 (en) * 2005-12-23 2010-09-09 Chemimage Corporation Chemical Imaging Explosives (CHIMED) Optical Sensor using SWIR
US8368880B2 (en) 2005-12-23 2013-02-05 Chemimage Corporation Chemical imaging explosives (CHIMED) optical sensor using SWIR
US20110080577A1 (en) * 2006-06-09 2011-04-07 Chemlmage Corporation System and Method for Combined Raman, SWIR and LIBS Detection
US20110237446A1 (en) * 2006-06-09 2011-09-29 Chemlmage Corporation Detection of Pathogenic Microorganisms Using Fused Raman, SWIR and LIBS Sensor Data
US8582089B2 (en) 2006-06-09 2013-11-12 Chemimage Corporation System and method for combined raman, SWIR and LIBS detection
US20110089323A1 (en) * 2009-10-06 2011-04-21 Chemlmage Corporation System and methods for explosives detection using SWIR
US9103714B2 (en) 2009-10-06 2015-08-11 Chemimage Corporation System and methods for explosives detection using SWIR
US8729502B1 (en) 2010-10-28 2014-05-20 The Research Foundation For The State University Of New York Simultaneous, single-detector fluorescence detection of multiple analytes with frequency-specific lock-in detection
US20120147224A1 (en) * 2010-12-08 2012-06-14 Canon Kabushiki Kaisha Imaging apparatus
US11684249B2 (en) * 2011-07-07 2023-06-27 Boston Scientific Scimed, Inc. Imaging system for endoscope

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US20060091311A1 (en) 2006-05-04
US7317516B2 (en) 2008-01-08
US20060049354A1 (en) 2006-03-09
US6734962B2 (en) 2004-05-11
US7068357B2 (en) 2006-06-27
US20060164640A1 (en) 2006-07-27
US20040159789A1 (en) 2004-08-19
US7061606B2 (en) 2006-06-13
US20060151702A1 (en) 2006-07-13
US7268862B2 (en) 2007-09-11
US7019296B2 (en) 2006-03-28
US20060157652A1 (en) 2006-07-20
US7436500B2 (en) 2008-10-14
US20020113210A1 (en) 2002-08-22
US7123360B2 (en) 2006-10-17
US20060192956A1 (en) 2006-08-31
US20060033026A1 (en) 2006-02-16

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