Efficient Parametric Yield Extraction for Multiple Correlated Non-Normal Performance Distributions of Analog/RF Circuits

In this paper we propose an efficient numerical algorithm to estimate the parametric yield of analog/RF circuits with consideration of large-scale process variations. Unlike many traditional approaches that assume Normal performance distributions, the proposed approach is especially developed to handle multiple correlated non-Normal performance distributions, thereby providing better accuracy than other traditional techniques. Starting from a set of quadratic performance models, the proposed parametric yield extraction conceptually maps multiple correlated performance constraints to a single auxiliary constraint using a MAX(ldr) operator. As such, the parametric yield is uniquely determined by the probability distribution of the auxiliary constraint and, therefore, can be easily computed. In addition, a novel second-order statistical Taylor expansion is proposed for an analytical MAX(ldr) approximation, facilitating fast yield estimation. Our numerical examples in a commercial BiCMOS process demonstrate that the proposed algorithm provides 2~3times error reduction compared with a Normal- distribution-based method, while achieving orders of magnitude more efficiency than the Monte Carlo analysis with 10⁴ samples.