Neural network diagnosis of IC faults
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The authors present 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.<>Keywords
This publication has 5 references indexed in Scilit:
- The multilayer perceptron as an approximation to a Bayes optimal discriminant functionIEEE Transactions on Neural Networks, 1990
- Neural network classification: a Bayesian interpretationIEEE Transactions on Neural Networks, 1990
- Learning in Artificial Neural Networks: A Statistical PerspectiveNeural Computation, 1989
- On the approximate realization of continuous mappings by neural networksNeural Networks, 1989
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989