Decision selection and learning for an ´all-solutions ATPG engine´

´All-solutions ATPG´ based methods have found applications in model checking sequential circuits, and they can also improve the defect coverage of a test-suite, by generating distinct multiple-detect patterns. Conventional decision selection heuristics and learning techniques for an ATPG engine were originally developed to ´quickly´ find any available (single) solution. Such decision selection heuristics may not be the best for an ´all-solutions ATPG´ engine, where all the solutions need to be found. In this paper, we explore new techniques to guide an ´all-solutions ATPG engine´. We first present a new decision selection heuristic that makes use of the ´connectivity of gates´ in the circuit in order to obtain a compact solution-set. Next, we analyze the ´symmetry in search-states´ that was exploited in ´success-driven learning´ and extend it to prune conflict sub-spaces as well. Finally, we propose a new metric that determines the use of learnt information a priori. This information is stored and used efficiently during ´success driven learning´. Experimental results show that we can compute the complete solution-set with our new heuristics for large ISCAS´89 and ITC´99 circuits, where conventional guidance heuristics fail.