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Proceedings Paper

Bare board test optimization using ART neural network in PCB production
Author(s): Huiyang Zhou; Aihua Li; Nianhong Wan; Liangsheng Qu
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Paper Abstract

In this paper, we present an optimal test method using the adaptive resonance technique (ART) neural network for bare printed circuit boards (PCB). In this method the electrical net on a board is taken as the basic test unit. By extracting electrical net features from the CAD data file, net characteristic vectors can be formed. Using the characteristic vector as the input to the ART network, the electrical nets can be clustered into different classes. Analyzing the long time memory matrix of the ART network, we can further determine the physical meaning of each class, and then make a proper test choice of the electrical nets.

Paper Details

Date Published: 28 August 1995
PDF: 5 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217530
Show Author Affiliations
Huiyang Zhou, Xi'an Jiaotong Univ. (China)
Aihua Li, Xi'an Jiaotong Univ. (China)
Nianhong Wan, Xi'an Jiaotong Univ. (China)
Liangsheng Qu, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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