Delay Defect Diagnosis Research Articles

Defect Diagnosis via Segment Delay Learning

This brief presents a scalable, high-quality delay defect diagnosis method based on segment delay estimation. Several recent studies have assumed that accurate segment delay recovery leads to a better diagnosis method, and they predict segment delays using Gaussian prior distributions on them. We show that the assumption is not necessarily true and propose to rank segments by the probability of the occurrence for the estimated delays. When random localized defects are considered, prior distributions on segment delays can have a long tail, but all the previous studies fail to model this region properly. We propose to modify the standard deviations of the Gaussian priors depending on the defect size. Our experiment shows that one of our methods achieves ~14% first hit rank improvements and 100× speedup on average over a previous method based on linear programming.

Delay defect diagnosis based upon a statistical timing model – the first step

The problem of diagnosing delay defects is defined using a statistical timing model. The differences between delay defect diagnosis and traditional logic defect diagnosis are illustrated. Different diagnosis algorithms are proposed and their performance evaluated via statistical defect injection and statistical delay fault simulation. With a statistical timing analysis framework developed in the past, new concepts in delay defect diagnosis are demonstrated and experimental results are discussed based upon benchmark circuits.