Cell-Aware Diagnosis of Customer Returns Using Bayesian Inference

Safa Mhamdi1, Patrick Girard2, Arnaud Virazel3, Alberto Bosio4, Aymen Ladhar5
1LIRMM - University of Montpellier, 2LIRMM / CNRS, 3LIRMM, 4Lyon Institute of Nanotechnology, 5STMicroelectronics


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 silicon test chip have proven the efficacy of our flow in terms of diagnosis accuracy and resolution.