基于故障覆盖的超大规模集成电路可测试性轮廓估计

H. Farhat, H. Saidian
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引用次数: 1

摘要

提出了一种利用随机故障覆盖数据估计电路可测性轮廓的新方法。他们最近发展了故障覆盖率和可测试性之间的关系。然而,它们的可测性轮廓估计是基于输入向量的未知分布,并使用具有先验均匀检测概率分布的贝叶斯定理。可测性剖面建模为一系列脉冲函数,并根据故障覆盖数据估计每个脉冲函数的强度。在三个大型ISCAS基准电路上给出的实验结果反映了这些估计的准确性。应用包括:可测试性分析的覆盖率预测,测试长度预测,以及通过故障采样生成测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Testability profile estimation of VLSI circuits from fault coverage
The authors present a new method of estimating the testability profile of a circuit from its random fault coverage data. They have recently developed a relationship between fault coverage and testability profile. However, their testability profile estimates were based on unknown distribution of input vectors and used Bayes theorem with a priori uniform detection probability distribution. The testability profile is modeled as a series of impulse functions and the strength of each estimated from fault coverage data. Experimental results given on three of the large ISCAS benchmark circuits reflect the accuracy of these estimates. Applications include: coverage prediction from testability analysis, prediction of test length, and test generation by fault sampling.<>
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