PADRE: Physically-Aware Diagnostic Resolution Enhancement

Abstract

Diagnosis is the first step of IC failure analysis. The conventional objective of identifying the failure locations has been augmented with various physically-aware techniques that are intended to improve both diagnostic resolution and accuracy. Despite these advances, it is often the case however that resolution, i.e., the number of locations or candidates reported by diagnosis, exceeds the number of actual failing locations. Imperfect resolution greatly hinders any follow-on, information-extraction analyses (e.g., physical failure analysis, volume diagnosis, etc.) due to the resulting ambiguity. To address this major challenge, a novel, unsupervised learning methodology that uses ordinarily-available tester and simulation data is described that significantly improves resolution with virtually no negative impact on accuracy. Simulation experiments using a variety of fault types (SSL, MSL, bridges, opens and cell-level input-pattern faults) reveal that the number of failed ICs that have perfect resolution can be more than doubled, and overall resolution is improved by 22%. Application to silicon data also demonstrates significant improvement in resolution (38% overall and the number of chips with ideal resolution is nearly tripled) and verification using PFA demonstrates that accuracy is maintained.