Content-Based Image Retrieval for Semiconductor Process Characterization
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Content-Based Image Retrieval for Semiconductor Process Characterization Kenneth W. Tobin Oak Ridge National Laboratory, P.O. Box 2008, Building 3500, MS-6010, Oak Ridge, TN 37831-6010, USA Email: [email protected]
Thomas P. Karnowski Oak Ridge National Laboratory, P.O. Box 2008, Building 3500, MS-6010, Oak Ridge, TN 37831-6010, USA Email: [email protected]
Lloyd F. Arrowood Oak Ridge National Laboratory, P.O. Box 2008, Building 3500, MS-6010, Oak Ridge, TN 37831-6010, USA Email: [email protected]
Regina K. Ferrell Oak Ridge National Laboratory, P.O. Box 2008, Building 3500, MS-6010, Oak Ridge, TN 37831-6010, USA Email: [email protected]
James S. Goddard Oak Ridge National Laboratory, P.O. Box 2008, Building 3500, MS-6010, Oak Ridge, TN 37831-6010, USA Email: [email protected]
Fred Lakhani International SEMATECH, 2706 Montopolis Dr., Austin, TX 78741-6499, USA Email: [email protected] Received 14 August 2001 and in revised form 12 February 2002 Image data management in the semiconductor manufacturing environment is becoming more problematic as the size of silicon wafers continues to increase, while the dimension of critical features continues to shrink. Fabricators rely on a growing host of image-generating inspection tools to monitor complex device manufacturing processes. These inspection tools include optical and laser scattering microscopy, confocal microscopy, scanning electron microscopy, and atomic force microscopy. The number of images that are being generated are on the order of 20,000 to 30,000 each week in some fabrication facilities today. Manufacturers currently maintain on the order of 500,000 images in their data management systems for extended periods of time. Gleaning the historical value from these large image repositories for yield improvement is diļ¬cult to accomplish using the standard database methods currently associated with these data sets (e.g., performing queries based on time and date, lot numbers, wafer identification numbers, etc.). Researchers at the Oak Ridge National Laboratory have developed and tested a content-based image retrieval technology that is specific to manufacturing environments. In this paper, we describe the feature representation of semiconductor defect images along with methods of indexing and retrieval, and results from initial field-testing in the semiconductor manufacturing environment. Keywords and phrases: content-based image retrieval, semiconductor manufacturing, image indexing, automatic defect classification, approximate nearest neighbors.
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INTRODUCTION
The ability to manage large image databases has been a topic of growing research. Imagery is being generated and maintained for a large variety of applications including remote
sensing, architectural and engineering design, geographic information systems, and weather forecasting. Content-based image retrieval (CBIR) is a technology that is being developed to address these application areas [1]. CBIR refers to
Content-Based Image Retrieval for Semiconductor Process Characterizat
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