Every Failure Is A Lesson: Utilizing All Failure Samples To Deliver Tuning-Free Efficient Yield Evaluation

Yield estimation and optimization have become increasingly important for circuit design as technology nodes scale down. Simple yet well-established minimal norm importance sampling (MNIS) still serves as an industrial standard due to its robustness and reliability. In this study, we generalize the classic MNIS and propose Every Failure Is A Lesson (EFIAL) to utilize every failure sample (instead of one in MNIS) to construct the proposal distribution. EFIAL is completely tuning-free and the update computation complexity is only O(M) (M is the number of failure samples) by utilizing the blessing of dimensionality. The idea of EFIAL is then extended to the state-of-the-art (SOTA) pre-sampling method, onion sampling, to significantly boost efficiency, by up to 9.08x (4.68x on average). Extensive evaluations against SOTA yield estimation methods reveal that EFIAL achieves a speedup of up to 13.54x (5.16x on average) and an accuracy improvement of up to 24.91%.