Diagnosis Outcome Preview through Learning
This work was presented at the VLSI Test Symposium (VTS), 2019.
Authors: Chenlei Fang, Qicheng Huang, Soumya Mittal, Shawn Blanton
Summary: Logic diagnosis can be time-consuming and produce ineffective information for further investigation of yield loss. It would therefore be desirable to have a preview of diagnosis outcomes beforehand, which helps engineers allocate diagnosis resources in a more efficient way. In this work, random forest classification and regression techniques are used to predict three aspects of potential diagnosis outcomes: existence of multiple defects, diagnosis resolution, and runtime magnitude.