Unsupervised Learning Provides Intelligence for Testing Hard to Detect Faults

Finding tests for hard-to-detect (HTD) faults in complex designs is challenging. Researchers use testability measures to guide automatic test pattern generation (ATPG) programs to improve fault detection efficiency. However, each measure favors the detection of specific faults in the same circuit. Principal component analysis (PCA), an unsupervised learning technique, has been used to combine several algorithmic testability measures. Guidance from the PCA measure was found to uniformly improve the ATPG efficiency with fewer backtracks, lower ATPG CPU time, detection of many HTD faults, fewer aborted faults, and increased fault coverage. The present work shows that these benefits continue further when we also included topological factors like fan-in and fanout cone base widths, fanout reconvergence data, and even-odd inversions on reconverging paths in a new-PCA measure. This work opens the venue for ongoing improvements.