Adaptive Bayesian Diagnosis of Intermittent Faults

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 %.