A Neural-Network-Based Fault Classifier

In order to reduce the number of defective parts and increase yield, especially in early stages of production, systematic defects must be identified and corrected as soon as possible. This paper presents a technique to move defect classification to the earliest phase of volume testing without any special diagnostic test patterns. A neural-network-based fault classifier is described, which is able to raise a warning, if the frequency of certain defect mechanisms increases. Only in this case more sophisticated diagnostic patterns or the even more expensive physical failure analysis have to be applied. The fault classification method presented here is able to extract underlying fault types with high confidence by identifying relevant features from the circuit topology and from logic simulation.