Improving Scan Chain Diagnostic Accuracy Using Multi-Stage Artificial Neural Networks

Diagnosis of intermittent scan chain failures remains a hard problem. We demonstrate that Artificial Neural Networks (ANNs) can be used to achieve significantly higher accuracy. The key is to take on domain knowledge and use a multi-stage process incorporating ANNs with gradually refined focuses. Experimental results on benchmark circuits show that this method is, on average, 20% more accurate than a state-of-the-art commercial tool for intermittent stuck-at faults, and improves the hit rate from 25.3% to 73.9% for some test-case.