CACOP - A RANDOM PATTERN TESTABILITY ANALYZER

In this paper a new method called CACOP for the detection probability analyses of random test patterns is proposed. Considering computationa l complexity, CACOP is a compromise between O(n(2)) testability analys es like full-range cutting algorithm (FRCA) and linear time testabilit y analyses like controllability observability program (COP), By propag ating bounds of controllabilities and observabilities, CACOP can deter mine the detection probability lower bound (DPLB) efficiently, The DPL Bs derived by CACOP are potentially higher (and thus more accurate) th an by FRCA; in addition, CACOP is computationally more efficient than FRCA. The conventional linear time testability analyses cannot guarant ee the derivation of DPLBs. On the contrary. CACOP can achieve the goa l with tolerable increase in computing complexity.