Explaining and Interpreting Machine Learning CAD Decisions: An IC Testing Case Study

Prashanth Krishnamurthy, Animesh Basak Chowdhury, Benjamin Tan, Farshad Khorrami, Ramesh Karri

Abstract: We provide a methodology to explain and interpret machine learning decisions in Computer-Aided Design (CAD) flows. We demonstrate the efficacy of the methodology to the VLSI testing case. Such a tool will provide designers with insight into the "black box" machine learning models/classifiers through human readable sentences based on normally understood design rules or new design rules. The methodology builds on an intrinsically explainable, rule-based ML framework, called Sentences in Feature Subsets (SiFS), to mine human readable decision rules from empirical data sets. SiFS derives decision rules as compact Boolean logic sentences involving subsets of features in the input data. The approach is applied to test point insertion problem in circuits and compared to the ground truth and traditional design rules.