Principal Component Analysis in Machine Intelligence-Based Test Generation

摘要

In a machine intelligence (MI)-based automatic test pattern generator (ATPG), an artificial neural network (ANN) may guide decisions that would otherwise rely on some heuristic. Heuristics use circuit-specific data such as gate types, logic depth, fan-out data, or various testability measures. Treating these data collectively as a multivariate statistic of circuit topology, this study extracts principal components (PCs). A subset of PCs is then used to train the ANN that facilitates algorithmic decisions in ATPG. This reduces the ANN complexity and enhances ATPG efficiency. Results on benchmark circuits show the benefit of reduced CPU time.