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Proceedings Paper

Automatic defect classification for integrated circuits
Author(s): Paul B. Chou; A. Ravishankar Rao; Martin C. Sturzenbecker; Virginia H. Brecher
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Paper Abstract

While initial detection of defects is the most critical function of inspection, automatic classification of detected defects is becoming increasingly desirable. The key to better process control is reliable process measurement. The classification of defects provides valuable process diagnosis information. The hope is that machines can perform this task more reliably than humans. However, there are many problems in automating defect classification, and many of these are related to the central problems in artificial intelligence, such as knowledge representation, inferencing, and dealing with uncertainty. In this paper we pay special attention to the issues arising in the Automatic Defect Classification (ADC) of integrated circuits. We first discuss technical and system requirements, followed by an outline of the technical challenges to be overcome to develop flexible and powerful ACD tools which can be quickly customized on a user level for diverse applications.

Paper Details

Date Published: 6 May 1993
PDF: 9 pages
Proc. SPIE 1907, Machine Vision Applications in Industrial Inspection, (6 May 1993); doi: 10.1117/12.144802
Show Author Affiliations
Paul B. Chou, IBM Thomas J. Watson Research Ctr. (United States)
A. Ravishankar Rao, IBM Thomas J. Watson Research Ctr. (United States)
Martin C. Sturzenbecker, IBM Thomas J. Watson Research Ctr. (United States)
Virginia H. Brecher, IBM Thomas J. Watson Research Ctr. (United States)

Published in SPIE Proceedings Vol. 1907:
Machine Vision Applications in Industrial Inspection
Frederick Y. Wu; Benjamin M. Dawson, Editor(s)

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