Bare board test optimization using ART neural network in PCB production

Huiyang Zhou; Aihua Li; Nianhong Wan; Liangsheng Qu

doi:10.1117/12.217530

28 August 1995 Bare board test optimization using ART neural network in PCB production

Huiyang Zhou, Aihua Li, Nianhong Wan, Liangsheng Qu

Proceedings Volume 2620, International Conference on Intelligent Manufacturing; (1995) https://doi.org/10.1117/12.217530
Event: International Conference on Intelligent Manufacturing, 1995, Wuhan, China

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.

Citation Download Citation

Huiyang Zhou, Aihua Li, Nianhong Wan, and Liangsheng Qu "Bare board test optimization using ART neural network in PCB production", Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); https://doi.org/10.1117/12.217530

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