Improving ATPG Through Abort-Driven Dynamic Learning (Addle)

Static learning has been used to improve structural Automatic Test Pattern Generation (ATPG) results by reducing backtracks and thus aborted faults. However, static learning is limited to simple relationships and can generate massive learning data, most of which is not needed during ATPG. We present a new dynamic learning technique to learn only needed relationships that can include multiple gates and time frames. We show improved coverage and pattern count on industrial design.