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

Zhanglei Wang, K. Chakrabarty
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引用次数: 22

Abstract

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
一种有效的测试模式选择方法,以减少测试数据量和测试应用时间来提高缺陷覆盖率
使用n个检测测试集进行测试,其中一个故障被n (n > 1)个输入模式检测,这种方法越来越被提倡以增加缺陷覆盖率。然而,n检测测试集的数据量通常太大,导致高测试时间和测试器内存需求。测试集选择对于确保从大批量生产测试环境中的大型测试集中选择最有效的模式是必要的。测试选择在时间受限的晶圆排序环境中也很有用。作者使用概率故障模型和输出偏差理论来选择测试集,即使用输出偏差度量来对候选测试模式进行排序,而不使用故障分级。为了证明所选模式的质量,给出了在ISCAS基准电路中电阻式桥接故障和非反馈式零电阻桥接故障的实验结果。我们的结果表明,对于相同的测试长度,基于输出偏差选择的模式比使用其他几种方法选择的模式更有效
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