A Static Test Compaction Method Based on GCN Assisted Fault Gate Classification

Static test compaction aims to reduce the number of generated test patterns after automatic test pattern generation (ATPG) to enable one pattern to detect more faults. However, existing traditional algorithms require the establishment and maintenance of a fault detection profile to obtain essential faults, identified as those detectable exclusively by a single pattern (denoted as 1-D), incurring substantial computational overhead. We propose a novel fault gates classification approach based on graph convolutional network (GCN). By categorizing fault gates into with and without hard-to-detect faults, we selectively construct a partial fault detection profile only for the fault gates with hard-to-detect faults. Partial fault detection profile effectively reduces the time spent on establishing and maintaining it, as well as the time cost of obtaining essential faults. The experiment shows that our improved method can increase pattern reduction efficiency while accelerating, and its impact on fault coverage can be ignored. Compared with the original algorithm, the maximum acceleration ratio is 6.44×, and the number of patterns is reduced by up to 20.68%.