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Diagnosing out-of-specification failures in mixed-signal circuits has become increasingly challenging due to: (1) failures caused by interactions between input-signal conditions and design uncertainties, and (2) the need to identify critical input and uncertainty conditions that cause these regions. We propose a simulation-driven approach that first uses ensemble learning to extract if − then rules that naturally solve both problems. By ranking, pruning and clustering these rules, we then construct non-linear failure regions which can be directly employed for pre-silicon debug, as demonstrated on a phase-locked loop circuit. Furthermore, these regions can be used to guide test pattern generation and/or assist with post-silicon debug.

Growing circuit complexity and design uncertainty has made it difficult to predict whether large circuits meet target property specifications. To address this, we conservatively approximate the failure probability estimate by defining an interval that bounds this probability. Doing so using an arbitrary sampling distribution requires a learner. Given that the learner's knowledge is imperfect, the interval must first capture its uncertainty. An ensemble of such learners can then be used to compensate for the bias. Lastly, we develop an adaptive sampling scheme to tighten the obtained interval with increased simulation resources, thus controlling the accuracy vs. turn-around-time trade-off.