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Proceedings Paper

Neural-based production yield prediction: an RBF-based approach
Author(s): Kurosh Madani; Ghislain de Tremiolles; Erin Williams; Pascal Tannhof
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Paper Abstract

Prediction and modeling in the case of non linear systems (or processes), especially of complex industrial processes are known being a class of involved problems. In this paper, we deal with the production yield prediction dilemma in VLSI manufacturing. An RBF neural networks based approach and its hardware implementation on a ZISC neural board have been presented. Experimental results comparing our approach with an expert have been reported and discussed.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342904
Show Author Affiliations
Kurosh Madani, Univ. Paris XII (France)
Ghislain de Tremiolles, Univ. Paris XII and IBM France (France)
Erin Williams, IBM France (United States)
Pascal Tannhof, IBM France (France)


Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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