Machine Intelligence for Efficient Test Pattern Generation

Abstract

This study examines machine intelligence’s (MI) ability to enhance automatic test pattern generation (ATPG) by reducing backtracks. In lieu of a conventional heuristic to decide backtracing directions, this study uses an artificial neural network (ANN) trained through PODEM on hard-to-detect faults. Training data contains topological data, testability measures, and backtracking history, and when trained on this data, the ANN guides backtracing in directions unlikely to backtrack. When trained with a single feature (e.g., COP), ATPG performance is comparable to conventional PODEM, and using multiple features further reduces backtracks and ATPG CPU time.