Improving Volume Diagnosis and Debug with Test Failure Clustering and Reorganization

Mu-Ting Wu, Cheng-Sian Kuo, C. Li, Chris Nigh, Gaurav Bhargava
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引用次数: 0

Abstract

Volume diagnosis and debug play a key role in identifying systematic test failures caused by manufacturing defectivity, design marginalities, and test overkill. However, diagnosis tools often suffer from poor diagnosis resolution. In this paper, we propose techniques to improve diagnosis resolution by test failure clustering and reorganization. The effectiveness of our techniques is demonstrated on two industrial designs in cutting-edge process nodes and verified by targeted analysis and testing. The number of suspects is reduced by 3.1x and 575.2x on average. The proposed techniques can be implemented using existing commercial diagnosis tools with runtime overheads below 1%.
用测试故障聚类和重组改进卷诊断和调试
批量诊断和调试在识别由制造缺陷、设计边际和测试过量引起的系统测试失败方面起着关键作用。然而,诊断工具往往存在诊断分辨率差的问题。在本文中,我们提出了通过测试故障聚类和重组来提高诊断分辨率的技术。我们技术的有效性在尖端工艺节点的两个工业设计中得到了证明,并通过有针对性的分析和测试进行了验证。犯罪嫌疑人平均减少3.1倍,平均减少575.2倍。所提出的技术可以使用现有的商业诊断工具来实现,运行时开销低于1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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