Approximate property checking of mixed-signal circuits

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.