An optimal test pattern selection method to improve the defect coverage

It is well known that n-detection test sets are effective to detect unmodeled defects and improve the defect coverage. However, in these sets, each of the n-detection test patterns has the same importance on the overall test set performance. In other words, the test pattern that detects a fault for the first time plays the same important role as the test pattern that detects that fault for the (n)-th time. In this paper, we propose a linear programming-based optimal test pattern selection method which aims at reducing the overall defect part level (DPL). Using resistive bridge faults as surrogates, our experimental results on ISCAS85 circuits demonstrate the proposed test pattern selection method achieves higher defect coverage than traditional n-detection method