Adaptive Bayesian Diagnosis of Intermittent Faults
Adaptive Bayesian Diagnosis of Intermittent Faults
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DOI：
10.1007/s10836-014-5477-1
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Laura Rodríguez Gómez;A. Cook;T. Indlekofer;S. Hellebrand;H. Wunderlich
中科院分区：
文献类型：
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作者：
Laura Rodríguez Gómez;A. Cook;T. Indlekofer;S. Hellebrand;H. Wunderlich
随着瞬时错误率的增加，区分间歇性和瞬时故障尤其具有挑战性。除了粒子撞击之外，在机会主义计算的架构和高变化下的技术中观察到相对高的瞬态错误率。本文提出了一种在嵌入式测试或内建自测试过程中，根据中间特征将故障分为永久性故障、间歇性故障和瞬时性故障的方法，永久性故障可以通过重复测试来确定。在许多情况下，可以通过失败的测试会话的数量来识别间歇性和瞬时故障。对于剩余的故障，贝叶斯分类技术已被开发，这是适用于大型数字电路。这些方法的组合能够识别间歇性故障的概率超过98%。
With increasing transient error rates, distinguishing intermittent and transient faults is especially challenging. In addition to particle strikes relatively high transient error rates are observed in architectures for opportunistic computing and in technologies under high variations. This paper presents a method to classify faults into permanent, intermittent and transient faults based on some intermediate signatures during embedded test or built-in self-test.Permanent faults are easily determined by repeating test sessions. Intermittent and transient faults can be identified by the amount of failing test sessions in many cases. For the remaining faults, a Bayesian classification technique has been developed which is applicable to large digital circuits. The combination of these methods is able to identify intermittent faults with a probability of more than 98 %.