Towards Smarter Diagnosis: A Learning-based Diagnostic Outcome Previewer
This work is published in the ACM Transactions on Design Automation of Electronic Systems (TODAES), 2020.
Authors: Qicheng Huang, Chenlei Fang, Soumya Mittal, Shawn Blanton
Summary: A learning-based previewer is proposed that 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.