The present invention relates to the field of automated mineralogy, and in particular to the use of charged particle beams in automated mineralogy systems.
Mineral analysis systems, such as the QEMSCAN and MLA available from FEI Company, Hillsboro, Oreg., have been used for many years to analyze mineral samples. To determine the type and relative quantity of minerals present in a mine, a sample in the form of small granules, is fixed in epoxy and placed in a vacuum chamber. An electron beam is directed toward a sample and, in a process called “energy dispersive x-ray spectroscopy” or “EDS,” the energies of x-rays coming from the sample in response to the electron beam are measured and plotted in a histogram to form a spectrum. The measured spectrum can be compared to the known spectra of various elements to determine which elements and minerals are present, and in what proportion.
It takes considerable time to accumulate an x-ray spectrum. When an electron in the primary beam impacts the sample, the electron loses energy by a variety of mechanisms. One energy loss mechanism includes transferring the electron energy to an inner shell electron, which can be ejected from the atom as a result. An outer shell electron will then fall into the inner shell, and a characteristic x-ray may be emitted. The energy of the characteristic x-ray is determined by the difference in energies between the inner shell and the outer shell. Because the energies of the shells are characteristic of the element, the energy of the x-ray is also characteristic of the material from which it is emitted. When the number of x-rays at different energies is plotted on a graph, one obtains a characteristic spectrum, such as the spectrum of pyrite shown in
Other emissions besides characteristic x-rays are detectable when an electron beam impacts a sample surface. Emitted background or bremsstrahlung radiation x-rays are spread over a wide range of frequencies and can obscure characteristic x-ray peaks. Secondary electrons, Auger electrons, elastically and inelastically forward or back scattered electrons, and light can be emitted from the surface upon impact of a primary electron beam and can be used to form an image of the surface or to determine other properties of the surface. Backscattered electrons are typically detected by a solid state detector in which each backscattered electron is amplified as it creates many electron-hole pairs in a semiconductor detector. The backscattered electron detector signal is used to form an image as the beam is scanned, with the brightness of each image point determined by the number of backscattered electrons detected at the corresponding point on the sample as the primary beam moves across the sample.
Backscattering of electrons depends on the atomic number of the elements in the surface and upon the geometric relationship between the surface, the primary beam, and the detector. Obtaining a backscattered electron image requires collecting only a sufficient number of electrons at each point to produce a reasonable contrast between points having different properties and therefore is significantly quicker than obtaining a sufficient number of x-rays to compile a complete spectrum at each point. Also, the probability of an electron being backscattered is greater than the probability of the electron causing the emission of a characteristic x-ray of a particular frequency. Obtaining a backscattered electron image typically takes less time than acquiring sufficient x-rays to obtain an analyzable spectrum at a single dwell point.
In one mode of operating the MLA system, an image is first acquired using a backscattered electron detector, and the image is then processed to identify regions that appear from the contrast to have the same elemental composition. The beam is then positioned at the centroid of each identified region for a longer dwell time to collect an x-ray spectrum representative of the region.
When performing automated mineralogy on difficult samples using x-ray and back-scattered electron (BSE) information, BSE accuracy and repeatability are critical to differentiating minerals that have similar chemical formulas. For example, when analyzing iron ore it is important to accurately detect and differentiate between hematite (Fe2O3) and magnetite (Fe3O4). Although magnetite and hematite can be easily distinguished qualitatively using optical microscopy, quantitative characterization by automated scanning electron microscopy/energy dispersive x-ray spectroscopy (SEM-EDS), such as MLA, is challenging because hematite and magnetite are similar in their chemical composition and BSE brightness. The x-ray spectra for these minerals are nearly identical when collected on standard silicon drift detectors (SDD) with energy ranges of 20 eV at low x-ray counts.
An object of the invention is to provide an improved method and apparatus for identifying unknown compounds in a sample material. Embodiments of the present invention are directed to a method for determining the mineral content of a sample using an electron microscope. The method includes directing an electron beam toward an area of interest of a sample, the area of interest comprising an unknown composition of minerals. The working distance between the backscattered electron detector of the microscope and the area of interest of the sample is determined. Compensation is made for the difference between the working distance and a predetermined working distance in which the predetermined working distance being the working distance that provides desired grayscale values for detected backscattered electrons. One way of compensating for working distance variation is to used an autofocus feature of the microscope to adjust the working distance. Backscattered electrons from the area of interest of the sample are then detected.
Other embodiments of the present invention are directed to a system for determining the mineral content of a sample using an electron microscope. The system includes a scanning electron microscope, one or more energy dispersive x-ray detectors, one or more backscatter electron detectors, and a system controller. The system controller includes a computer processor and a non-transitory computer-readable medium. The non-transitory computer-readable medium is encoded with computer instructions that, when executed by the computer processor, cause the system controller to direct an electron beam toward an area of interest of a sample, the area of interest comprising an unknown composition of minerals. The working distance between the backscattered electron detector of the microscope and the area of interest of the sample is determined. Compensation is made for the difference between the working distance and a predetermined working distance in which the predetermined working distance being the working distance that provides desired grayscale values for detected backscattered electrons. One way of compensating for working distance variation is to used an autofocus feature of the microscope to adjust the working distance. Backscattered electrons from the area of interest of the sample are then detected.
The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims.
For a more thorough understanding of the present invention, and advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
Embodiments of the present invention include a method for automatically configuring a scanning electron microscope (SEM)
In the MLA approach, mineral phases are first distinguished by their BSE grayscale levels during an on-line segmentation operation and then by their energy dispersive x-ray (EDX) spectrum. Minerals associated with a similar BSE values are segmented into a single phase. The results of a BSE mineral analysis determines the mineral phases and boundaries in a sample. This method of image segmentation allows BSE detectors to outline the regions of varying compounds in a sample which provides an easy means of separating the compounds and determining the overall content of a sample.
For a given set of beam sample current, contrast, and brightness settings, BSE grayscale values are most impacted by changes to the distance between the sample surface and the BSE detector (the “working distance”). Therefore, to obtain the bimodal peak shown in
In alternative embodiments, other means of determining the working distance may be used, such as a laser interferometer or a capacitive sensor. In other alternative embodiments, the effects of physical working distance variations are modeled to resultant BSE level and autofocus or other means is used to determine the current physical working distance then calculate a BSE adjustment. This embodiment is useful for systems with no Z axis adjustment capability.
For example, a sample holder may have fourteen or more “hockey puck” style samples. The center of each sample is measured to determine how high the sample is and determine the working distance between the surface of the sample and the BSE detector. In a six-inch stage, these measurements can differ by 500 micrometers or more between samples. Since only 50 micrometers of working distance or focal distance can affect the results of a BSE analysis by one grayness level, it becomes very important to maintain a constant working distance between the sample surface and the backscatter electron detector. With the measurements of the samples being potentially off by 500 micrometers or more due to imperfections in the samples, the auto focus feature is seen to be critical to the device and method disclosed. The auto focus feature will determine the precise height, on the z-axis, that every sample point will have, and adjust the height of the stage or sample to ensure a consistent working distance and focal distance.
A system controller 533 controls the operations of the various parts of scanning electron beam system 500. The vacuum chamber 510 is evacuated with ion pump 568 and mechanical pumping system 569 under the control of vacuum controller 532.
Electron beam 532 can be focused onto sample 502, which is on movable X-Y stage 504 within lower vacuum chamber 510. When the electrons in the electron beam strike sample 502, the sample gives off x-rays whose energy correlated to the elements in the sample. X-rays 572 having energy inherent to the elemental composition of the sample are produced in the vicinity of the electron beam incident region. Emitted x-rays are collected by x-ray detector 540, preferably an energy dispersive detector of the silicon drift detector type, although other types of detectors could be employed, which generates a signal having an amplitude proportional to the energy of the detected x-ray. Backscattered electrons are detected by backscatter electron detector 542, preferably a segmented BSE detector.
Output from detector 540 is amplified and sorted by the processor 520, which counts and sorts the total number of X-rays detected during a specified period of time, at a selected energy and energy resolution, and a channel width (energy range) of preferably between 10-20 eV per channel. Processor 520 can comprise a computer processor; operator interface means (such as a keyboard or computer mouse); program memory 522 for storing data and executable instructions; interface means for data input and output, executable software instructions embodied in executable computer program code; and display 544 for displaying the results of a multivariate spectral analysis by way of video circuit 592.
Processor 520 can be a part of a standard laboratory personal computer, and is typically coupled to at least some form of computer-readable media. Computer-readable media, which include both volatile and nonvolatile media, removable and non-removable media, may be any available medium that can be accessed by processor 520. By way of example and not limitation, computer-readable media comprise computer storage media and communication media. Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by processor 520.
Program memory 522 can include computer storage media in the form of removable and/or non-removable, volatile and/or nonvolatile memory and can provide storage of computer-readable instructions, data structures, program modules and other data. Generally, the processor 520 is programmed by means of instructions stored at different times in the various computer-readable storage media of the computer. Programs and operating systems are typically distributed, for example, on floppy disks or CD-ROMs. From there, they are installed or loaded into the secondary memory of a computer. At execution, they are loaded at least partially into the computer's primary electronic memory. The invention described herein includes these and other various types of computer-readable storage media when such media contain instructions or programs for implementing the steps described below in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
An x-ray spectrum obtained as described above can be stored in a portion of memory 522, such as the measured spectra memory portion 523. Data template memory portion 524 stores data templates, such as known spectra of elements or, in some embodiments, known diffraction patterns of materials. Weighing Factor Memory portion 525 stores weighting factor for each of the data templates, the weighting factors combining with the data templates to produce a calculated spectrum approximating the measured spectrum. The weighting factors correlated to the abundance in the sample of the element corresponding to the data template. Processor 520 uses the methods described above to minimize an error value which represents the different between the measured pattern and the combination of the data templates and weighting factors.
Auto focus system 560 is then used to determine the working distance between sample 502 and backscatter electron detector 542 (step 606). The system then determines whether the working distance is within a predetermined acceptable range (step 608). The predetermined acceptable range is the narrow range of working distances for which, given set of beam sample current, contrast, and brightness settings, produce the BSE grayscale values best discriminate between the elements or compounds that are being identified. If the working distance is within the predetermined acceptable range, then the system proceeds with backscatter electron detection (step 610). If the working distance is not within the predetermined acceptable range, then the system adjusts the working distance between sample 502 and backscatter electron detector 542 so that the working distance is within the predetermined acceptable range (step 612). In one embodiment, sample stage 504 is translated in the z-axis to move sample 502 closer to or farther from the backscatter electron detector 542. If the working distance is smaller than the predetermined acceptable range, then sample stage 504 is translated in the z-axis away from the backscatter electron detector 542 so the working distance is increased until the working distance is within the predetermined acceptable range. If the working distance is larger than the predetermined acceptable range, then sample stage 504 is translated in the z-axis toward the backscatter electron detector 542 so the working distance is decreased until the working distance is within the predetermined acceptable range. In alternative embodiments, backscatter electron detector 542 is translated in the z-axis to bring the working distance within the predetermined acceptable range. Once the working distance is within the predetermined acceptable range, then the system proceeds with backscatter electron detection (step 610). The process ends at stop block 614.
Auto focus system 560 is then used to determine the working distance between sample 502 and backscatter electron detector 542 (step 706). The system then determines whether the working distance is within a predetermined acceptable range (step 708). The predetermined acceptable range is the narrow range of working distances which produce the BSE grayscale values that best discriminate between the elements or compounds that are being identified. If the working distance is within the predetermined acceptable range, then the system proceeds with backscatter electron detection (step 710). If the working distance is not within the predetermined acceptable range, then the system calculates a BSE adjustment level based on modeled working distance variations and the measured working distance (step 712). The system proceeds with backscatter electron detection (step 710) and adjusts the detected BSE levels using the calculated BSE adjustment level (step 714). The process ends at stop block 716.
Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.
Number | Name | Date | Kind |
---|---|---|---|
3519824 | Weinstock et al. | Jul 1970 | A |
4037101 | Okumura et al. | Jul 1977 | A |
4136429 | Brandes | Jan 1979 | A |
4242586 | Warble | Dec 1980 | A |
4435507 | Stenkvist | Mar 1984 | A |
4476386 | Reid et al. | Oct 1984 | A |
4587424 | Grau | May 1986 | A |
4592082 | Pawloski | May 1986 | A |
4807148 | Lacey | Feb 1989 | A |
4834943 | Yoshiyama | May 1989 | A |
4839516 | Freeman et al. | Jun 1989 | A |
5084618 | Ito | Jan 1992 | A |
5555198 | Asano | Sep 1996 | A |
RE35514 | Albrecht et al. | May 1997 | E |
5741707 | Herron et al. | Apr 1998 | A |
5798525 | Benizri-Carl et al. | Aug 1998 | A |
5841149 | Spink et al. | Nov 1998 | A |
5866903 | Morita et al. | Feb 1999 | A |
5906919 | Garini et al. | May 1999 | A |
5991028 | Cabib et al. | Nov 1999 | A |
6018587 | Cabib | Jan 2000 | A |
6066459 | Garini et al. | May 2000 | A |
6072178 | Mizuno | Jun 2000 | A |
6093930 | Boyette, Jr. et al. | Jul 2000 | A |
6122343 | Pidcock | Sep 2000 | A |
6140643 | Brown et al. | Oct 2000 | A |
6166380 | Kitagawa et al. | Dec 2000 | A |
6282301 | Haskett | Aug 2001 | B1 |
6341257 | Haaland | Jan 2002 | B1 |
6377652 | Sturm | Apr 2002 | B1 |
6466929 | Brown et al. | Oct 2002 | B1 |
6470335 | Marusak | Oct 2002 | B1 |
6584413 | Keenan et al. | Jun 2003 | B1 |
6658143 | Hansen et al. | Dec 2003 | B2 |
6674894 | Parker et al. | Jan 2004 | B1 |
6687620 | Haaland et al. | Feb 2004 | B1 |
6711503 | Haaland | Mar 2004 | B2 |
6723871 | Tada et al. | Apr 2004 | B2 |
6724940 | Qian et al. | Apr 2004 | B1 |
6765205 | Ochiai et al. | Jul 2004 | B2 |
6842702 | Haaland et al. | Jan 2005 | B2 |
6888920 | Blank et al. | May 2005 | B2 |
6977723 | Lemmo et al. | Dec 2005 | B2 |
6993170 | Johnson et al. | Jan 2006 | B2 |
7053365 | Shimomura | May 2006 | B2 |
7061605 | Lemmo et al. | Jun 2006 | B2 |
7108970 | Levinson | Sep 2006 | B2 |
7132652 | Testoni | Nov 2006 | B1 |
7139415 | Finkbeiner | Nov 2006 | B2 |
7161672 | Gornushkin et al. | Jan 2007 | B2 |
7243030 | Reeve et al. | Jul 2007 | B2 |
7400770 | Keaton et al. | Jul 2008 | B2 |
7436510 | Grun et al. | Oct 2008 | B2 |
7490009 | Gottlieb et al. | Feb 2009 | B2 |
7790465 | Otvos | Sep 2010 | B2 |
7804059 | Harrison | Sep 2010 | B2 |
7930106 | Carrick | Apr 2011 | B2 |
7979217 | Gottlieb et al. | Jul 2011 | B2 |
8060173 | Goode, Jr. et al. | Nov 2011 | B2 |
8119991 | Harrison | Feb 2012 | B2 |
20020102102 | Watanabe et al. | Aug 2002 | A1 |
20020169589 | Banki et al. | Nov 2002 | A1 |
20030136907 | Takane et al. | Jul 2003 | A1 |
20040011958 | Wright et al. | Jan 2004 | A1 |
20040027350 | Kincaid et al. | Feb 2004 | A1 |
20040099805 | Ochiai et al. | May 2004 | A1 |
20040147830 | Parker et al. | Jul 2004 | A1 |
20050037515 | Nicholson et al. | Feb 2005 | A1 |
20050060868 | McMurtry | Mar 2005 | A1 |
20050165290 | Kotsianti et al. | Jul 2005 | A1 |
20050258366 | Honda et al. | Nov 2005 | A1 |
20060043294 | Yamaguchi et al. | Mar 2006 | A1 |
20060051251 | Desrosiers et al. | Mar 2006 | A1 |
20060227922 | Pop et al. | Oct 2006 | A1 |
20060291619 | Statham | Dec 2006 | A1 |
20070109557 | Saito et al. | May 2007 | A1 |
20070181793 | Harrison | Aug 2007 | A1 |
20070279629 | Grun et al. | Dec 2007 | A1 |
20080137082 | Grun et al. | Jun 2008 | A1 |
20080250881 | Dona | Oct 2008 | A1 |
20090039261 | Toyoda et al. | Feb 2009 | A1 |
20100044566 | Donitz et al. | Feb 2010 | A1 |
20100060893 | Norton et al. | Mar 2010 | A1 |
20110144922 | Corbett et al. | Jun 2011 | A1 |
20110147587 | Yang et al. | Jun 2011 | A1 |
20110301869 | Gottlieb et al. | Dec 2011 | A1 |
20120019648 | Hoshino et al. | Jan 2012 | A1 |
20120154668 | Kimura et al. | Jun 2012 | A1 |
20120314206 | Spizig et al. | Dec 2012 | A1 |
20130015351 | Kooijman et al. | Jan 2013 | A1 |
20130134307 | Routh, Jr. | May 2013 | A1 |
20130254948 | Hartong et al. | Sep 2013 | A1 |
Number | Date | Country |
---|---|---|
100498309 | Jun 2009 | CN |
05087707 | Apr 1993 | JP |
08015185 | Jan 1996 | JP |
10312763 | Nov 1998 | JP |
2000249668 | Sep 2000 | JP |
2001006597 | Jan 2001 | JP |
2001066269 | Mar 2001 | JP |
2002189005 | Jul 2002 | JP |
2005274352 | Oct 2005 | JP |
2009-075071 | Apr 2009 | JP |
2011113640 | Jun 2011 | JP |
2054660 | Feb 1996 | RU |
9905503 | Feb 1999 | WO |
2008013597 | Jan 2008 | WO |
2009100404 | Aug 2009 | WO |
Entry |
---|
Figueroa et al., “Advanced Discrimination of Hematite and Magnetite by Automated Mineralogy”, Proceedings of 10th Int'l Congress for Applied Mineralogy (ICAM), Aug. 1-5, 2011. |
Figueroa, German, et al., ‘Advanced discrimination of hematite and magnetite by automated minerology,’ 10th International Congress for Applied Mineralogy, Aug. 1-5, 2011, pp. 197-204. |
Miller, Jeff, “Jeff's (Fairly Comprehensive) Raith Usage Notes,” Marcus Group, Harvard University, Unknown date Version 20040929.1, 12 pages. |
Newbury, Dale E., “Chemical compositional mapping by microbeam analysis at the micrometer scale and finer,” Microelectronics Journal, 1997. pp. 489-508, vol. 28. |
Newbury, Dale “Pushing the Envelope with SEM/SDD-EDS Mapping: X-ray Spectrum Image Mapping in 30 Seconds or Less, but What are the Real Limits?” Proc. of SPIE, 2010, 9 pages, vol. 7729. |
Oversluizen, Tom, et al., “Kinematic mounting systems for National Synchrotron Light Source beamlines and experiments,” Rev. Sci. Instrum., Jan. 1992, pp. 1285-1288, vol. 63 No. 1. |
Pirrie, Duncan, et al., “Rapid quantitative mineral and phase analysis using automated scanning electron microscopy (QemSCAN); potential applications in forensic geoscience,” Forensic Geoscience: Principles, Techniques and Applications, 2004, pp. 123-136. |
Pye, Kenneth, et al., “Forensic Geoscience: Principles, Techniques and Applications,” The Geological Society, Mar. 3 & 4, 2003, 55 pages. |
Slocum, A. H., “Kinematic couplings for precision fixturing—Part I: Formulation of design parameters,” Precision Engineering, Apr. 1988, pp. 85-92, vol. 10 No. 2. |
Slocum, A. H., et al., “Kinematic couplings for precision fixturing—Part 2: Experimental determination of repeatability and stiffness,” Precision Engineering, Jul. 1988, pp. 115-122, vol. 10 No. 3. |
Slocum, Alexander H., “Design of three-groove kinematic couplings,” Precision Engineering, Apr. 1992, pp. 67-77, vol. 14, No. 2. |
Slocum, Alexander, “Kinematic couplings: a review of design principles and applications,” International Journal of Machine Tools & Manufacture, 2010, pp. 310-327, vol. 50. |
Sutherland, D. N., et al., “Application of Automated Quantitative Mineralogy in Mineral Processing,” Minerals Engineering, 1991, pp. 753-762, vol. 4 No. 7-11. |
Sutherland, D. N., “Image Analysis for Off-Line Characterisation of Mineral Particles and Prediction of Processing Properties,” Part. Part. Syst. Charact., 1993, pp. 271-274, vol. 10. |
Unknown, “Raith e—LINE User Guide,” online, Nov. 2009,18 pages. |
Van Hoek, Corrie J.G., et al., “A SEM-EDS Study of Cultural Heritage Objects with Interpretation of Constituents and Their Distribution Using PARC Data Analysis,” Microsc. Microanal. 2011, pp. 656-660, vol. 17. |
Zelenika, S., et al., “Kinematic Couplings for Synchrotron Radiation Instrumentation,” 2nd International Workshop on Mechanical Engineering Design of Synchrotron Radiation Equipment and Instrumentation, Sep. 5-6, 2002, 9 pages. |
Ashton, Edward A., “Multialgorithm solution for automated multispectral target detection,” Opt. Eng., Apr. 1999, pp. 717-724, vol. 38, No. 4. |
Benz, Ursula C., et al., “Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information,” ISPRS Journal of Photogrammetry & Remote Sensing, 2004, pp. 239-258, vol. 58. |
Creelman, Robert A., et al., “A scanning electron microscope method for automated, quantitative analysis of mineral matter in coal,” International Journal of Coal Geology, 1996, pp. 249-269, vol. 30. |
Unknown, “Energy-dispersive X-ray spectroscopy,” Wikepedia, http://en.wikipedia.org/wiki/Engergy—Dispersive—Spectroscopy, obtained Jul. 29, 2013, 3 pages. |
Fandrich, Rolf, et al., “Modern SEM-based mineral liberation analysis,” Int. J. Miner. Process., 2007, pp. 310-320, vol. 84. |
Furse, J.E., “Kinematic design of fine mechanisms in instruments,” J. Phys. E: Sci. Instrum, 1981, pp. 264-272, vol. 14. |
Ghassemian, Hassan, et al., “Object-Oriented Feature Extraction Method for Image Data Compaction,” IEEE Control Systems Magazine, Jun. 1998, pp. 42-48. |
Gottlieb, P., et al., “The Automatic Identification and Quantification of Silver Minerals,” XVIII International Mineral Processing Congress, May 23-28, 1993, pp. 475-481. |
Gottlieb, P. et al., “Using Quantitative Electron Microscopy for Process Mineralogy Applications,” JOM, Apr. 2000, pp. 24-25. |
Gu, Ying, “Automated Scanning Electron Microscope Based Mineral Liberation Analysis, an Introduction to JKMRC/FEI Mineral Liberation Analyser,” Journal of Mineral & Materials Characterization & Engineering, 2003, pp. 33-41, vol. 2, No. 1. |
Hale, Layton C., et al., “Optimal design techniques for kinematic couplings,” Journal of the International Societies for Precision Engineering and Nanotechnology, 2001, pp. 114-127, vol. 25. |
Hazel, Geoffrey G., “Object-level Processing of Spectral Imagery for Detection of Targets and Changes Using Spatial-Spectral-Temporal Techniques,” Proceeding of the SPIE, 2001, pp. 380-390, vol. 4381. |
Jana, Dipayan, “Sample Preparation Techniques in Petrographic Examinations of Construction Materials: A State-of the-Art Review,” Proceedings of the twenty-eighth Conference on Cement Microscopy, Apr. 30-May 4, 2006, 48 pages. |
Lapicki, Adam, et al., “Kinematic sample mounting system for accurate positioning of transferrable samples,” J. Vac. Sci. Technol. A, Sep./Oct. 2000, pp. 2603-2605, vol. 18 No. 5. |
Meyer, K., et al., “Qualitative and quantitative mixture analysis by library search: infrared analysis of mixtures of carbohydrates,” Analytica Chimica Acta, 1993, pp. 161-171, vol. 281. |
Number | Date | Country | |
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20140183357 A1 | Jul 2014 | US |