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Proceedings Paper

Neural net selection of features for defect inspection
Author(s): Kenji Sasaki; David P. Casasent; Sanjay S. Natarajan
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Paper Abstract

An artificial neural network (ANN) fed with optically generated features is applied to IC inspection. The data used are characters with defects in them that model those expected in IC patterns. The ANN is used in training to select the best features. This results the required number of neurons needed during defect testing. Simulation results are provided for four types of defects using optical Fourier Wedge-Ring (WR) sampled Fourier and Hough feature spaces.

Paper Details

Date Published: 1 February 1991
PDF: 6 pages
Proc. SPIE 1384, High-Speed Inspection Architectures, Barcoding, and Character Recognition, (1 February 1991); doi: 10.1117/12.25327
Show Author Affiliations
Kenji Sasaki, Toshiba Corp. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)
Sanjay S. Natarajan, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 1384:
High-Speed Inspection Architectures, Barcoding, and Character Recognition
Michael J. W. Chen, Editor(s)

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