Effectively Using Machine Learning to Expedite System Level Test Failure Debug
Effectively Using Machine Learning to Expedite System Level Test Failure Debug
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有效利用机器学习加速系统级测试故障调试
DOI：
10.1109/itc44170.2019.9000163
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Christina Carter
中科院分区：
文献类型：
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作者：
Luis D. Rojas;Kevin C. Hess;Christina Carter
在本文中，提出了一种基于机器学习的算法来对系统级测试故障进行分类。系统级测试故障首先被建模为多维特征空间中的一个点。然后，使用多类支持向量机分类器，通过一对一的方法将此类故障分类为预先确定的故障类别。当所提出的算法自动应用于大量失败的系统级测试故障时，可以生成百万分之一的缺陷率故障趋势，并用于确定调试活动的优先级。所提出的算法在最新的Intel®Xeon®14nm产品线中成功实现，分类准确率达到80%，平均分类时间为20秒。
In this contribution, a machine learning based algorithm to classify system level test failures is proposed. A system level test failure is first modeled as a point in a multidimensional feature space. Then, such failure is classified into a pre-determined failure class, using the multi-class Support Vector Machine classifier, via the one-versus-one approach. When the proposed algorithm is automatically applied to a population of failing system level test failures, defect part per million failure trends can be produced, and used to prioritize debug activities. The proposed algorithm was successfully implemented in the latest Intel®Xeon®14nm product line, with a classification accuracy of 80% and an average classification time of 20 seconds.