Enabling High-Dimensional Bayesian optimization for Efficient Failure Detection of Analog and Mixed-Signal Circuits

With increasing design complexity and stringent robustness requirements in application such as automotive electronics, analog and mixed-signal (AMS) verification becomes a key bottleneck. Rare failure detection in a high-dimensional parameter space using minimal expensive simulation data is a major challenge. We address this challenge under a Bayesian learning framework using Bayesian optimization (BO). We formulate the failure detection as a BO problem where a chosen acquisition function is optimized to select the next (set of) optimal simulation sampling point(s) such that rare failures may be detected using a small amount of data. While providing an attractive black-box solution to design verification, in practice BO is limited in its ability in dealing with high-dimensional problems. We propose to use random embedding to effectively reduce the dimensionality of a given verification problem to improve both the quality of BO-based optimal sampling and computational efficiency. We demonstrate the success of the proposed approach on detecting rare design failures under high-dimensional process variations which are completely missed by competitive smart sampling and BO techniques without dimension reduction.