Fast Candidate Screening for Post-diagnosis Refinement

Oftentimes fault candidates produced by logic diagnosis are too many to effectively guide the follow-on failure analysis. In this work, we propose a novel two-stage fast screening method to sift through large amount of candidates in the fault callout outputted by the commercial diagnosis tool. Candidates that are unlikely to be true ones are re-assigned lower ranks or discarded as diagnosis noise. Experimental results based on benchmark designs from various sources show that within the new categorical ranking list, the number of remaining candidates is only 53.78% of the original ones produced by the diagnosis tool, while ensuring an average accuracy of 91.65%, leading to a 1.98 × increase in diagnostic resolution. Our fast screening method only takes seconds of model training and computation, while using no other additional resource besides necessary inputs for generic logic diagnosis.