Fast diagnosis of integrated circuit faults using feedforward neural networks

Abstract

Presents experimental results which show that feedforward neural networks are suitable for analog IC fault diagnosis. The results suggest that feedforward networks provide a cost-efficient method for IC fault diagnosis in large-scale production. The authors compare the diagnostic accuracy and the computational requirements of a simple feedforward network against that of Gaussian maximum likelihood and K-nearest neighbors classifiers. The feedforward network was found to provide an order-of-magnitude improvement in diagnostic speed while consistently performing as well as or better than any of the other classifiers in terms of accuracy. >