Implementation of model-based intelligent next-generation test generator using neural networks
Steven M. Singer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
This work, investigated the use of Neural Network technology to simulate faults and to generate input/output patterns used to diagnose electronic circuits via pattern classification. There are several types of circuits (i.e., digital, analog, hybrid (digit-analog), RF, and microwave). This study focused on digital circuits while maintaining the posture of considering other types in the future with similar solutions. The main focus was to investigate a methodology to model complex digital components using system identification neural network architectures. Using those components in software, a digital circuit was assembled. Faults indicating stuck at 1 or 0 was propagated through the circuit (one at a time). Input and output sequences were combined for each situations modeled and those sequences were classified to the known modeled behavior using the Adaptive Resonance Theory neural network algorithm. In addition, a data reduction methodology was established to generate input patterns required for the recognition scheme.