Minimizing defective part level using a linear programming-based optimal test selection method

Recent probabilistic test generation approaches have proven that detecting single stuck-at-faults multiple times is effective at reducing the defective part level (DPL). Unfortunately, these test generation strategies increase the number of test patterns. In this paper, we present a novel linear programming-based method to accelerate the optimal selection of test sets to minimize the defective part level based upon the MPG-D model. Our experimental results show that the proposed method is on average 300 times faster than the existing test pattern selection method.