Artificial neural-network model-based observers

Abstract

Describes a pseudorandom testing scheme for fault diagnosis of analog integrated circuits. The goal is to implement a BIST technique with both a built-in pattern generator and a response analyzer for fault diagnosis. We have chosen a diagnostic framework for the analog ICs using a pseudorandom noise generator as the test-pattern generator and a model-based observer to detect and diagnose faults. The observer is implemented through a multilayer feedforward ANN trained with a back-error propagation (BEP) algorithm. Both the test-pattern generator and the model-based observer proposed in this article can be implemented either on- or offline depending on the need of the application and silicon area overhead.