Information-theoretic and statistical methods of failure log selection for improved diagnosis
Information-theoretic and statistical methods of failure log selection for improved diagnosis
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用于改进诊断的故障日志选择的信息论和统计方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
L. Lingappan
中科院分区：
文献类型：
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作者：
Sarmad Tanwir;S. Prabhu;M. Hsiao;L. Lingappan
每个故障部件的诊断需要在测试设备上捕获的故障数据。然而，由于测试仪上的内存限制，人们通常不能存储每个被测试芯片的所有故障数据。因此，每个部件都使用截断的故障日志，而不是完整的日志。这种故障日志的截断可能会导致诊断的周转时间非常长，因为重要的故障点可能会从日志中删除。因此，即使在多次重复诊断之后，最终诊断的准确性和分辨率也可能受到影响。此外，现有的测试响应压缩技术虽然对测试很好，但要么对诊断产生不利影响，要么对所选故障模型的偏差高度敏感。在这种情况下，行业需要动态选择更好的故障日志来增强诊断。在本文中，我们提出了一些基于信息论的度量，这些度量可以帮助动态选择故障日志，以提高最终诊断的准确性和分辨率。我们还通过我们的实验结果报告了这些度量的有效性。
Diagnosis of each failed part requires the failed data captured on the test equipment. However, due to memory limitations on the tester, one often cannot store all the failed data for every chip tested. Consequently, truncated failure logs are used instead of complete logs for each part. Such truncation of the failure logs can result in very long turn-around times for diagnosis because important failure points may be removed from the log. Subsequently, the accuracy and resolution of final diagnosis may suffer even after multiple iterations of diagnosis. In addition, the existing test response compaction techniques though good for testing, either adversely affect diagnosis or are highly sensitive to deviation from the chosen fault model. In this context, the industry needs dynamic selection of better failure logs that enhances diagnosis. In this paper, we propose a number of metrics based on information theory that may help in selecting failure logs dynamically for improving the accuracy and resolution of final diagnosis. We also report on the efficacy of these metrics through the results of our experiments.