Diagnostic resolution improvement through learning-guided physical failure analysis
An accurate and high-resolution diagnosis enables physical failure analysis (PFA) to identify and understand the root-cause of integrated-circuit failure. Despite many existing techniques for improving diagnosis, resolution is still far from ideal, which hinders PFA and other analyses. To address this challenge, we extend the capability of PADRE (physically-Aware diagnostic resolution enhancement), a powerful machine learning based diagnosis resolution improvement technique, with a novel, active learning (AL) based PFA selection approach. An active-learning based PADRE (AL PADRE) selects the most useful defects for PFA in order to improve diagnostic resolution. AL PADRE provides an alternative to the normal PFA selection procedure, it improves the the accuracy of PADRE, and thus enables a more accurately improved resolution. AL PADRE is validated by both simulation-based experiment and silicon experiment. Simulation-based experiments show that by using AL PADRE, the number of PFAs required for increasing the accuracy to a stable level of 90% is reduced by more than 60% on average compared to baseline approach, and AL PADRE consistently outperforms the baseline approach for accuracy improvement in various scenarios. In the silicon experiment, by using AL PADRE, the number of chips needed to undergo PFA was reduced by more than 6x in order to increase diagnosis accuracy by more than 20%.
