An Efficient Test Pattern Selection Method for Improving Defect Coverage with Reduced Test Data Volume and Test Application Time

Testing using n-detection test sets, in which a fault is detected by n (n > 1) input patterns, is being increasingly advocated to increase defect coverage. However, the data volume for an n-detection test set is often too large, resulting in high testing time and tester memory requirements. Test set selection is necessary to ensure that the most effective patterns are chosen from large test sets in a high-volume production testing environment. Test selection is also useful in a time-constrained wafer-sort environment. The authors use a probabilistic fault model and the theory of output deviations for test set selection - the metric of output deviation is used to rank candidate test patterns without resorting to fault grading. To demonstrate the quality of the selected patterns, experimental results were presented for resistive bridging faults and non-feedback zero-resistance bridging faults in the ISCAS benchmark circuits. Our results show that for the same test length, patterns selected on the basis of output deviations are more effective than patterns selected using several other methods