Machine Intelligence for Efficient Test Pattern Generation
Soham Roy, Spencer K. Millican, Vishwani D. Agrawal
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.
Topics & Concepts
BacktrackingComputer scienceAutomatic test pattern generationTestabilityArtificial intelligenceArtificial neural networkHeuristicFeature engineeringFeature (linguistics)Machine learningTest dataDeep learningAlgorithmEngineeringReliability engineeringProgramming languagePhilosophyElectrical engineeringLinguisticsElectronic circuitVLSI and Analog Circuit TestingIntegrated Circuits and Semiconductor Failure AnalysisEngineering and Test Systems