Neural Network Diagnosis of IC Faults

This paper presents experimental results which show that feedforward neural networks are well suited for analog IC fault diagnosis. Their results suggest that feedforward networks provide a cost efficient method for IC fault diagnosis in a large scale production environment. They specifically 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 is 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. This makes the feedforward network classifier an excellent candidate for production line diagnosis of IC faults, where circuit verification time greatly influences total cost per part.