Decision selection and associated learning for computing all solutions in automatic test pattern generation (ATPG) and satisfiability

摘要

An all solutions automatic test pattern generation (ATPG) engine method uses a decision selection heuristic that makes use of the “connectivity of gates” in the circuit in order to obtain a compact solution-set. The “symmetry in search-states” is analyzed using a “Success-Driven Learning” technique which is extended to prune conflict sub-spaces. A metric is used to determine the use of learnt information a priori, which information is stored and used efficiently during “success driven learning”.