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Open Access

Content-Based Image Retrieval for Semiconductor Process Characterization

  • Kenneth W. Tobin1Email author,
  • Thomas P. Karnowski1,
  • Lloyd F. Arrowood1,
  • Regina K. Ferrell1,
  • James S. Goddard1 and
  • Fred Lakhani2
EURASIP Journal on Advances in Signal Processing20022002:215759

Received: 14 August 2001

Published: 24 July 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 difficult 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.


content-based image retrievalsemiconductor manufacturingimage indexingautomatic defect classificationapproximate nearest neighbors

Authors’ Affiliations

Oak Ridge National Laboratory, Oak Ridge, USA
International SEMATECH, Austin, USA


© Tobin et al. 2002