Using regression analysis for GA-based ATPG parameter optimization

Genetic algorithms have proven to be a viable solution to the NP-complete problem of test vector generation. However the parameters used to control GA-based ATPG can greatly affect test set size, fault coverage and CPU execution time. Knowing how a given set of parameters will affect each of these factors a priori allows for more efficient testing procedures. Over 1 million ATPG experiments were conducted on the ISCAS85 benchmark set exploring a wide range of parameter options. Although sequential circuit testing looms as the larger problem, investigating combinational circuits should provide direction as to where efforts should be focused. From our experiments, we derive regression-based equations utilizing circuit characteristics and various controllable parameters. Using these equations, the ATPG tool determines parameter values that maximize fault coverage while meeting constraints on CPU run times and test set size. For many circuits tested, fault coverage improved with a tolerable increase in CPU time