Neural-based production yield prediction: an RBF-based approach

Kurosh Madani; Ghislain de Tremiolles; Erin Williams; Pascal Tannhof

doi:10.1117/12.342904

22 March 1999 Neural-based production yield prediction: an RBF-based approach

Kurosh Madani, Ghislain de Tremiolles, Erin Williams, Pascal Tannhof

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342904
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Kurosh Madani, Ghislain de Tremiolles, Erin Williams, and Pascal Tannhof "Neural-based production yield prediction: an RBF-based approach", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342904

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available