A Deterministic-Statistical Multiple-Defect Diagnosis Methodology
A Deterministic-Statistical Multiple-Defect Diagnosis Methodology
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DOI：
10.1109/vts48691.2020.9107603
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Soumya Mittal;R. D. Blanton
中科院分区：
文献类型：
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作者：
Soumya Mittal;R. D. Blanton
软件诊断是定位和确定故障芯片中缺陷的过程。它是失效分析的基石，从而使成品率学习和监控成为可能。然而，由于错误掩蔽和去掩蔽效应以及解搜索过程的指数复杂性，多缺陷诊断是具有挑战性的。本文描述了一种称为MDLearnX的三阶段物理感知诊断方法，它可以有效地诊断多个缺陷，进而帮助加速设计和工艺开发。第一阶段确定类似于传统故障模型的缺陷。第二和第三阶段利用X-故障模型和机器学习来识别正确的候选者。彻底的故障注入和模拟实验的结果表明，MD-LearnX返回理想诊断的频率是商业诊断的两倍。它的有效性通过一个硅片实验得到了进一步的证明，与最先进的商业诊断相比，MD-LearnX平均每次诊断返回的候选项少5.3个，而不会损失准确性。
Software diagnosis is the process of locating and characterizing a defect in a failing chip. It is the cornerstone of failure analysis that consequently enables yield learning and monitoring. However, multiple-defect diagnosis is challenging due to error masking and unmasking effects, and exponential complexity of the solution search process. This paper describes a three-phase, physically-aware diagnosis methodology called MDLearnX to effectively diagnose multiple defects, and in turn, aid in accelerating the design and process development. The first phase identifies a defect that resembles traditional fault models. The second and the third phases utilize the X-fault model and machine learning to identify correct candidates. Results from a thorough fault injection and simulation experiment demonstrate that MD-LearnX returns an ideal diagnosis 2X more often than commercial diagnosis. Its effectiveness is further evidenced through a silicon experiment, where, on average, MD-LearnX returns 5.3 fewer candidates per diagnosis as compared to state-of-the-art commercial diagnosis without losing accuracy.