Enhancing topological ATPG with high-level information and symbolic techniques

This paper proposes a method to enhance topological ATPG algorithms by exploiting some information computed through symbolic techniques. Since symbolic techniques can only be applied to small circuits, suitable circuit portions (named macros) are first selected, and then symbolic techniques are used to analyze their state graphs. The topological ATPG algorithm benefits from this analysis to bound its search tree. Experimental results show that the pro posed approach is effective in reducing the required CPU time and increasing both the Fault Coverage and the Fault Efficiency. When high-level information about the circuit behavior and structure is available, it can be fruitfully exploited for macro selection.