Towards Smarter Diagnosis: A Learning-based Diagnostic Outcome Previewer

Abstract

Given the inherent perturbations during the fabrication process of integrated circuits that lead to yield loss, diagnosis of failing chips is a mitigating method employed during both yield ramping and high-volume manufacturing for yield learning. However, various uncertainties in the fabrication process bring a number of challenges, resulting in diagnosis with undesirable outcomes or low efficiency, including, for example, diagnosis failure, bad resolution, and extremely long runtime. It would therefore be very beneficial to have a comprehensive preview of diagnostic outcomes beforehand, which allows fail logs to be prioritized in a more reasonable way for smarter allocation of diagnosis resources. In this work, we propose a learning-based previewer, which is able to predict five aspects of diagnostic outcomes for a failing IC, including diagnosis success, defect count, failure type, resolution, and runtime magnitude. The previewer consists of three classification models and one regression model, where Random Forest classification and regression are used. Experiments on a 28 nm test chip and a high-volume 90 nm part demonstrate that the predictors can provide accurate prediction results, and in a virtual application scenario the overall previewer can bring up to 9× speed-up for the test chip and 6× for the high-volume part.