Cell-Aware Diagnosis of Customer Returns Using Bayesian Inference

Abstract

This paper presents a new cell-aware diagnosis flow that can be used to address a specific scenario (test protocol) one may encounter during diagnosis of customer returns. In this flow, we use a Bayesian classification method to precisely identify defect candidates. Experiments done on benchmark circuits as well as on a test chip from STMicroelectronics have proven the efficacy of our flow in terms of diagnosis accuracy and resolution.