Efficient Parametric Yield Estimation Over Multiple Process Corners via Bayesian Inference Based on Bernoulli Distribution

Zhengqi Gao, Jun Tao, Dian Zhou, Xuan Zeng

Abstract: Parametric yield estimation over multiple process corners plays an important role in robust circuit design. In this article, we propose a novel Bayesian inference method based on Bernoulli distribution (BI-BD) to efficiently estimate the multicorner yields for binary output circuit. The key idea is to encode the circuit performance correlation among different corners as our prior knowledge. Consequently, after combining a few simulation samples, the yield estimation over all corners can be calibrated via Bayesian inference based on iterative reweighted least squares (IRLS) and expectation maximization (EM). A circuit example demonstrates that the proposed BI-BD method can achieve up to 2.0 × cost reduction over the conventional Monte Carlo method without surrendering any accuracy.