Mr. Thomas P. Karnowski Profile

Thomas P. Karnowski

Research & Development Engineer

SPIE Involvement:

Author

Publications (33)

Proceedings Article | 6 June 2022 Poster + Paper

Poster + Paper

Training data selection for event classification in a highly variable environment

Anand Iyer, Garrison Flynn, Nidhi Parikh, Daniel Archer, Thomas Karnowski, Monica Maceira, Omar Marcillo, Andrew Nicholson, Will Ray, Randall Wetherington, Michael Willis

Proceedings Volume 12113, 1211325 (2022) https://doi.org/10.1117/12.2617153

KEYWORDS: Data modeling, Machine learning, Distance measurement, Performance modeling, Feature extraction, Acoustics, Sensors, Fluctuations and noise, Thermal sensing, Signal processing

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Proceedings Article | 18 March 2013 Paper

Paper

Automated detection of microaneurysms using robust blob descriptors

K. Adal, S. Ali, D. Sidibé, T. Karnowski, E. Chaum, F. Mériaudeau

Proceedings Volume 8670, 86700N (2013) https://doi.org/10.1117/12.2007913

KEYWORDS: Image quality, Image enhancement, Radon transform, Image contrast enhancement, Feature extraction, Image analysis, Computer aided diagnosis and therapy, Databases, Digital photography, Radon

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Proceedings Article | 13 March 2013 Paper

Paper

Steerable wavelet transform for atlas based retinal lesion segmentation

Sharib Ali, Kedir Adal, Désiré Sidibé, Edward Chaum, Thomas Karnowski, Fabrice Mériaudeau

Proceedings Volume 8669, 86693D (2013) https://doi.org/10.1117/12.2006357

KEYWORDS: Image segmentation, Eye, Macula, Gold, Optic nerve, Retina, Image processing, Image enhancement, Wavelet transforms, Principal component analysis

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Diabetic macular edema (DME) characterized by discrete white{yellow lipid deposits due to vascular leakage is one of the most severe complication seen in diabetic patients that cause vision loss in affected areas. Such vascular leakage can be treated by laser surgery. A regular follow{up and laser photocoagulation can reduce the risk of blindness by 90%. In an automated retina screening system, it is thus very crucial to make the segmentation of such hard exudates accurate and register these images taken over time to a reference co-ordinate system to make the necessary follow-ups more precise. We introduce a novel method of ethnicity based statistical atlas for exudates segmentation and follow-up. Ethnic background plays a significant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images. Such statistical atlas can thus help to provide a solution, simplify the image processing steps and increase the detection rate. In this paper, bright lesion segmentation is investigated and experimentally verified for the gold standard built from African American fundus images. 40 automatically generated landmark points on the major vessel arches with macula and optic centers are used to warp the retinal images. PCA is used to obtain a mean shape of the retinal major arches (both lower and upper). The mean of the co-ordinates of the macula and optic disk center are obtained resulting 42 landmark points and together they provide a reference co-ordinate frame ( or the atlas co-ordinate frame) for the images. The retinal funds images of an ethnic group without any artifact or lesion are warped to this reference co-ordinate frame from which we obtain a mean image representing the statistical measure of the chromatic distribution of the pigments in the eye of that particular ethnic group. 400 images of African American eye has been used to build such a gold standard for this ethnic group. Any test image of the patient of that ethnic group is first warped to the reference frame and then a distance map is obtained with this mean image. Finally, the post-processing schemes are applied on the distance map image to enhance the edges of the exudates. A multi-scale and multi-directional steerable filters along with the Kirsch edge detector was found to be promising. Experiments with the publicly available HEI-MED dataset showed the good performance of the proposed method. We achieved the lesion localization fraction (LLF) of 82.5% at 35% of non{lesion localization fraction (NLF) on the FROC curve.

Proceedings Article | 7 February 2011 Paper

Paper

Automatic firearm class identification from cartridge cases

Sridharan Kamalakannan, Christopher Mann, Philip Bingham, Thomas Karnowski, Shaun Gleason

Proceedings Volume 7877, 78770P (2011) https://doi.org/10.1117/12.872414

KEYWORDS: Firearms, Image segmentation, Machine vision, Visual process modeling, Head, Statistical modeling, Scene classification, Interferometers, Image processing, Hough transforms

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Proceedings Article | 5 May 2010 Paper

Paper

Design of dual-road transportable portal monitoring system for visible light and gamma-ray imaging

Thomas Karnowski, Mark Cunningham, James Goddard, Anil Cheriyadat, Donald Hornback, Lorenzo Fabris, Ryan Kerekes, Klaus-Peter Ziock, E. Craig Bradley, J. Chesser, W. Marchant

Proceedings Volume 7665, 76651J (2010) https://doi.org/10.1117/12.850191

KEYWORDS: Cameras, Imaging systems, Gamma radiation, Machine vision, Calibration, Video, Roads, Gamma ray imaging, Image resolution, Motion estimation

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Proceedings Article | 13 March 2010 Paper

Paper

Microaneurysms detection with the radon cliff operator in retinal fundus images

Luca Giancardo, Fabrice Mériaudeau, Thomas Karnowski, Kenneth Tobin, Yaqin Li, Edward Chaum

Proceedings Volume 7623, 76230U (2010) https://doi.org/10.1117/12.844442

KEYWORDS: Radon, Radon transform, Detection and tracking algorithms, Image segmentation, Eye, Retina, Cameras, Sensors, Capillaries, Image processing

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Proceedings Article | 29 January 2010 Paper

Paper

Motion estimation accuracy for visible-light/gamma-ray imaging fusion for portable portal monitoring

Thomas Karnowski, Mark Cunningham, James Goddard, Anil Cheriyadat, Donald Hornback, Lorenzo Fabris, Ryan Kerekes, Klaus-Peter Ziock, Timothy Gee

Proceedings Volume 7538, 75380F (2010) https://doi.org/10.1117/12.838428

KEYWORDS: Motion estimation, Imaging systems, Machine vision, Cameras, Gamma radiation, Error analysis, Gamma ray imaging, Roads, Stereoscopic cameras, Sensors

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Proceedings Article | 10 March 2006 Paper

Paper

Characterization of the optic disc in retinal imagery using a probabilistic approach

Kenneth Tobin, Edward Chaum, V. Govindasamy, Thomas Karnowski, Omer Sezer

Proceedings Volume 6144, 61443F (2006) https://doi.org/10.1117/12.641670

KEYWORDS: Image segmentation, Optic nerve, Retina, Image filtering, Image analysis, Pathology, Image processing, Eye, Visualization, Binary data

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Proceedings Article | 24 February 2005 Paper

Paper

A simple optical system for real-time size measurements of TRISO fuel pellets

Thomas Karnowski, Andrew Kercher, John Hunn, L. Curt Maxey

Proceedings Volume 5679, (2005) https://doi.org/10.1117/12.584296

KEYWORDS: Optical spheres, Particles, Calibration, Data acquisition, Receivers, Error analysis, Optical testing, Laser optics, Optical transfer functions, Fluctuations and noise

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SPIE Journal Paper | 1 July 2004

Semiconductor sidewall shape estimation

Philip Bingham, Jeffery Price, Kenneth Tobin, Thomas Karnowski

JEI, Vol. 13, Issue 03, (July 2004) https://doi.org/10.1117/12.10.1117/1.1763586

KEYWORDS: Feature extraction, Scanning electron microscopy, Principal component analysis, Image processing, Databases, Semiconductors, Semiconducting wafers, Error analysis, Imaging systems, Process control

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Proceedings Article | 20 May 2004 Paper

Paper

System considerations for maskless lithography

Thomas Karnowski, David Joy, Larry Allard, Lloyd Clonts

Proceedings Volume 5374, (2004) https://doi.org/10.1117/12.530899

KEYWORDS: Printing, Monte Carlo methods, Manufacturing, Error analysis, Maskless lithography, Chemical elements, Lithography, Semiconductors, Polymethylmethacrylate, Photomasks

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Lithographic processes for printing device structures on integrated circuits (ICs) are the fundamental technology behind Moore's law. Next-generation techniques like maskless lithography or ML2 have the advantage that the long, tedious and expensive process of fabricating a unique mask for the manufactured chip is not necessary. However, there are some rather daunting prblems with establishing ML2 as a viable commercial technology. The data rate necessary for ML2 to be competitive in manufacturing is not feasible with technology in the near future. There is also doubt that the competing technologies for the writing mechanisms and corresponding photoresist (or analogous medium) will be able to accurately produce the desired patterns necessary to produce multi-layer semiconductor devices. In this work, we model the maskless printing system from a signal processing point of view, utilizing image processing algorithms and concepts to study the effects of various real-world constraints and their implications for a ML2 system. The ML2 elements are discrete devices, and it is doubtful that their motion can be controlled to the level where a one-for-one element to exposed pixel relationship is allowable. Some level of sub-element resolution can be achieved with gray scale levels, but with the highly integrated manufacturing practices required to achieve massive parallelism, the most effective elements will be simple on-ofrf switches that fire a fixed level of energy at the target medium. Consequently gray-scale level devidces are likely not an option. Another problem with highly integrated manufacturing methods is device uniformity. Consequently, we analyze the redundant scanning array concept (RSA) conceived by Berglund et al. which can defeat many of these problems. We determine some basic equations governing its application and we focus on applying the technique to an array of low-energy electron emitters. Using the results of Monte Carlo simulations on electron beam profiles, we determine an empirical "impulse response" for each emitter and thus determine how each emission manifests itself in the final printed lithographic pattern. We apply methods to determine the best printable image for a variety of RSA geometries, including different levels of redundancy and achieved printer element spacing. We use concepts of total printing error to help quantify the printing quality. Through simulation, we report the effects of dead or missing elements. We also present some error analysis to account for non-ideal array positioning. Ultimately, we believe that printing quality should be the grounds for determining the necessary data rates to support competitive manufacturing with ML2 devices.

Proceedings Article | 15 July 2003 Paper

Paper

Sidewall structure estimation from CD-SEM for lithographic process control

Philip Bingham, Jeffery Price, Kenneth Tobin, Thomas Karnowski, Marylyn Bennett, E. Hal Bogardus, Michael Bishop

Proceedings Volume 5041, (2003) https://doi.org/10.1117/12.485229

KEYWORDS: Scanning electron microscopy, Finite element methods, Feature extraction, Databases, Semiconducting wafers, Critical dimension metrology, Sensors, Lithography, Image retrieval, Electron beams

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Proceedings Article | 22 May 2003 Paper

Paper

Estimating cross-section semiconductor structure by comparing top-down SEM images

Jeffery Price, Philip Bingham, Kenneth Tobin, Thomas Karnowski

Proceedings Volume 5011, (2003) https://doi.org/10.1117/12.479691

KEYWORDS: Semiconductors, Scanning electron microscopy, Principal component analysis, Feature extraction, Databases, Image processing, Lithography, Electron microscopes, Statistical analysis, Pattern recognition

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Proceedings Article | 1 May 2003 Paper

Paper

Semiconductor sidewall shape estimation using top-down CD-SEM image retrieval

Jeffery Price, Philip Bingham, Kenneth Tobin, Thomas Karnowski

Proceedings Volume 5132, (2003) https://doi.org/10.1117/12.514963

KEYWORDS: Antimony, Feature extraction, Semiconductors, Principal component analysis, Image retrieval, Scanning electron microscopy, Databases, Image processing, Image analysis, Algorithm development

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Proceedings Article | 12 July 2002 Paper

Paper

Detection of semiconductor defects using a novel fractal encoding algorithm

Shaun Gleason, Regina Ferrell, Thomas Karnowski, Kenneth Tobin

Proceedings Volume 4692, (2002) https://doi.org/10.1117/12.475642

KEYWORDS: Fractal analysis, Defect detection, Semiconductors, Computer programming, Image processing, Scanning electron microscopy, Databases, Photomasks, Image segmentation, Detection and tracking algorithms

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Proceedings Article | 12 July 2002 Paper

Paper

Using an image retrieval system for image data management

Thomas Karnowski, Kenneth Tobin, Regina Ferrell, William Jatko, Fred Lakhani

Proceedings Volume 4692, (2002) https://doi.org/10.1117/12.475648

KEYWORDS: Databases, Image retrieval, Image processing, Manufacturing, Image storage, Image classification, Semiconductors, Image enhancement, Imaging systems, Visualization

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Images of semiconductor defects are maintained in semiconductor yield-management systems to help diagnose problems that arise during the manufacturing process. A common problem in future systems is the number of images to maintain, which is increasing at an alarming rate due to the growing use of in-line and off-line imaging systems. A manufacturing-specific content-based image retrieval system, or Automated Image Retrieval (AIR) system, was developed by ORNL in coordination with International SEMATECH during 1998-1999. The system uses commercial databases to store image information and uses a customized indexing technology to rapidly retrieve similar images base don visual content. In addition to acting as a yield management tool based on storing and retrieving images, the system can be utilized as a tool for data management by helping determine when images are redundant in relation to previously stored data. Ideally this information can be used to time-stamp the data for future purging based on a variety of ratings such as 'long- term', 'mid-term', and 'short-term'. In some situations the feedback from the AIR system can even be used to omit the image entirely based on pre-existing close matches. In this paper we explore techniques for using the AIR system to assist in image data management. Experimental results are shown with simulated image data representing various degrees of image clustering of redundancy, and manufacturing image data accumulated during earlier field-testing of the AIR system at industry sites. Early results indicate substantial reductions in the size of industry databases may be achievable while continuing to maintain an adequate representation and history of the manufacturing process. To reduce the number of stored images, AIR technology can be used in place of, or as a guide for, the typical 'store for N months and purge' approach to image management. This approach will enhance the use of the image database, since the real bottleneck in such a procedure is the need to sort such massive amounts of stored data as opposed to actual disk space.

Proceedings Article | 12 July 2002 Paper

Paper

Industry survey of automatic defect classification technologies, methods, and performance

Kenneth Tobin, Fred Lakhani, Thomas Karnowski

Proceedings Volume 4692, (2002) https://doi.org/10.1117/12.475640

KEYWORDS: Manufacturing, Inspection, Semiconductors, Semiconductor manufacturing, Classification systems, Scanning electron microscopy, Optics manufacturing, Process control, Semiconducting wafers, Metals

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Proceedings Article | 5 June 2001 Paper

Paper

Field-test results of an image retrieval system for semiconductor yield learning

Thomas Karnowski, Kenneth Tobin, Lloyd Arrowood, Regina Ferrell, James Goddard, Fred Lakhani

Proceedings Volume 4275, (2001) https://doi.org/10.1117/12.429362

KEYWORDS: Image retrieval, Databases, Semiconducting wafers, Photomasks, Semiconductors, Inspection, Image processing, Image classification, Visualization, Scanning electron microscopy

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Proceedings Article | 5 June 2001 Paper

Paper

Integrated applications of inspection data in the semiconductor manufacturing environment

Kenneth Tobin, Thomas Karnowski, Fred Lakhani

Proceedings Volume 4275, (2001) https://doi.org/10.1117/12.429361

KEYWORDS: Manufacturing, Semiconducting wafers, Semiconductors, Image processing, Databases, Inspection, Image retrieval, Semiconductor manufacturing, Data processing, Image classification

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Proceedings Article | 2 June 2000 Paper

Paper

Survey of semiconductor data management systems technology

Kenneth Tobin, Thomas Karnowski, Fred Lakhani

Proceedings Volume 3998, (2000) https://doi.org/10.1117/12.386478

KEYWORDS: Manufacturing, Databases, Data storage, Process control, Semiconductors, Computer aided design, Semiconducting wafers, Semiconductor manufacturing, Data communications, Data processing

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Proceedings Article | 19 April 2000 Paper

Paper

Multiframe combination and blur deconvolution of video data

Timothy Gee, Thomas Karnowski, Kenneth Tobin

Proceedings Volume 3974, (2000) https://doi.org/10.1117/12.383016

KEYWORDS: Video, Video surveillance, Motion models, Deconvolution, Surveillance, Image analysis, Sensors, Image processing, Video processing, Image quality

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Proceedings Article | 21 March 2000 Paper

Paper

Accommodating multiple illumination sources in an imaging colorimetry environment

Kenneth Tobin, James Goddard, Martin Hunt, Kathy Hylton, Thomas Karnowski, Marc Simpson, Roger Richards, Dale Treece

Proceedings Volume 3966, (2000) https://doi.org/10.1117/12.380073

KEYWORDS: Metals, Spectroscopy, Image processing, Image segmentation, Imaging systems, Manufacturing, Spectrophotometry, Color difference, Colorimetry, Inspection

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Proceedings Article | 21 March 2000 Paper

Paper

Content-based image retrieval for semiconductor manufacturing

Thomas Karnowski, Kenneth Tobin, Regina Ferrell, Fred Lakhani

Proceedings Volume 3966, (2000) https://doi.org/10.1117/12.380070

KEYWORDS: Databases, Image retrieval, Image processing, Photomasks, Semiconductors, Semiconductor manufacturing, Image segmentation, Content based image retrieval, Feature extraction, Image storage

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Proceedings Article | 14 June 1999 Paper

Paper

Application of spatial signature analysis to electrical test data: validation study

Thomas Karnowski, Kenneth Tobin, Shaun Gleason, Fred Lakhani

Proceedings Volume 3677, (1999) https://doi.org/10.1117/12.350840

KEYWORDS: Semiconducting wafers, Manufacturing, Optics manufacturing, Image processing, Analytical research, Fuzzy logic, Library classification systems, Wafer-level optics, Feature extraction, Semiconductors

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This paper presents the result of the Spatial Signature Analysis (SSA) ELectrical-test (e-test) validation study that was conducted between February and June,1998. SSA is an automated procedure developed by researchers at the Oak Ridge National Laboratory to address the issue of intelligent data reduction while providing feedback on current manufacturing processes. SSA was initially developed to automate the analysis of optical defect data. Optical defects can form groups, or clusters, which may have a distinct shape. These patterns can reveal information about the manufacturing process. Optical defect SSA uses image processing algorithms and a classifier system to interpret and identify these patterns, or 'signatures'. SSA has been extended to analyze and interpret electrical test data. The algorithms used for optical defect SSA have been adapted and applied to e-test binmaps. An image of the binmap is created, and features such as geometric and invariant moments are extracted and presented to a pair-wise, fuzzy, k-NN classifier. The classifier itself was prepared by manually training, which consists of storing example signatures of interest in a library, then executing an automated process which treats the examples as prototype signatures. The training process include a procedure for automatically determining which features are most relevant to each class. The evaluation was performed by installing the SSA software as a batch process at three SEMATECH member company sites. Feedback from member company representatives was incorporated and classifiers were built to automatically assign label sot he binmap signatures. The three sites produced memory devices and microprocessors in a mature process fabrication environment. For all of these products, 5,620 signatures that encompassed approximately 552 wafers were human-classified and analyzed. The performance of the SSA E-test system indicates that the approach was successful in reliably classifying binmap signatures in a manner similar to the human expert.

Proceedings Article | 8 March 1999 Paper

Paper

Image retrieval in the industrial environment

Kenneth Tobin, Thomas Karnowski, Regina Ferrell

Proceedings Volume 3652, (1999) https://doi.org/10.1117/12.341138

KEYWORDS: Manufacturing, Image processing, Databases, Semiconductors, Scanning electron microscopy, Image retrieval, Image segmentation, Process control, Inspection, Optical semiconductors

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Proceedings Article | 8 March 1999 Paper

Paper

Imaging tristimulus colorimeter for the evaluation of color in printed textiles

Martin Hunt, James Goddard, Kathy Hylton, Thomas Karnowski, Roger Richards, Marc Simpson, Kenneth Tobin, Dale Treece

Proceedings Volume 3652, (1999) https://doi.org/10.1117/12.341131

KEYWORDS: Image segmentation, Imaging systems, Optical filters, Image processing, Cameras, Reflectivity, Image processing algorithms and systems, CCD cameras, Prototyping, Spectrophotometry

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Proceedings Article | 8 June 1998 Paper

Paper

Rapid yield learning through optical defect and electrical test analysis

Shaun Gleason, Kenneth Tobin, Thomas Karnowski, Fred Lakhani

Proceedings Volume 3332, (1998) https://doi.org/10.1117/12.308731

KEYWORDS: Semiconducting wafers, Manufacturing, Image processing, Wafer-level optics, Optics manufacturing, Fuzzy logic, Semiconductor manufacturing, Semiconductors, Library classification systems, Analytical research

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Proceedings Article | 2 February 1998 Paper

Paper

Rule-based inspection of printed green ceramic tape

David Patek, James Goddard, Thomas Karnowski, Douglas Lamond, Terry Hawkins

Proceedings Volume 3306, (1998) https://doi.org/10.1117/12.301234

KEYWORDS: Inspection, Computer aided design, Cameras, Ceramics, Printing, Image processing, Software development, Defect detection, Visualization, Composites

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Proceedings Article | 2 February 1998 Paper

Paper

Adaptation of the fuzzy k-nearest neighbor classifier for manufacturing automation

Kenneth Tobin, Shaun Gleason, Thomas Karnowski

Proceedings Volume 3306, (1998) https://doi.org/10.1117/12.301238

KEYWORDS: Manufacturing, Fuzzy logic, Classification systems, Pattern recognition, Inspection, Semiconductor manufacturing, Optical inspection, Optics manufacturing, Systems engineering

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Proceedings Article | 2 February 1998 Paper

Paper

Fuzzy logic connectivity in semiconductor defect clustering

Thomas Karnowski, Shaun Gleason, Kenneth Tobin

Proceedings Volume 3306, (1998) https://doi.org/10.1117/12.301244

KEYWORDS: Fuzzy logic, Semiconducting wafers, Semiconductors, Manufacturing, Distance measurement, Image processing, Algorithm development, Software development, Fuzzy systems, Inspection

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Proceedings Article | 7 July 1997 Paper

Paper

Automatic classification of spatial signatures on semiconductor wafer maps

Kenneth Tobin, Shaun Gleason, Thomas Karnowski, Susan Cohen, Fred Lakhani

Proceedings Volume 3050, (1997) https://doi.org/10.1117/12.275936

KEYWORDS: Semiconducting wafers, Manufacturing, Fuzzy logic, Particles, Optics manufacturing, Wafer-level optics, Analytical research, Semiconductors, Visualization, Contamination

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Proceedings Article | 15 April 1997 Paper

Paper

Feature analysis and classification of manufacturing signatures based on semiconductor wafer maps

Kenneth Tobin, Shaun Gleason, Thomas Karnowski, Susan Cohen

Proceedings Volume 3029, (1997) https://doi.org/10.1117/12.271241

KEYWORDS: Semiconducting wafers, Fuzzy logic, Feature selection, Iris, Manufacturing, Analytical research, Semiconductors, Image classification, Image processing, Optical inspection

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Proceedings Article | 21 May 1996 Paper

Paper

Image paradigm for semiconductor defect data reduction

Kenneth Tobin, Shaun Gleason, Thomas Karnowski, Hamed Sari-Sarraf, Marylyn Bennett

Proceedings Volume 2725, (1996) https://doi.org/10.1117/12.240084

KEYWORDS: Semiconducting wafers, Image segmentation, Image processing, Photomasks, Manufacturing, Semiconductors, Particles, Statistical analysis, Defect detection, Binary data

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Showing 5 of 33 publications

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