Linear circuit fault diagnosis using neuromorphic analyzers

This paper presents a method of analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a simple mechanism for automatic test pattern generation while reducing test time. A new diagnosis framework consisting of a white noise generator and an artificial neural network for response analysis and classification is proposed. This approach moves the diagnosis of analog circuits closer to the goal of built-in test. Networks of reasonable dimension are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances.