Evaluation of analog/RF test measurements at the design stage
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Behavior-level yield enhancement approach for large-scaled analog circuits
Proceedings of the 47th Design Automation Conference
Stochastic analog circuit behavior modeling by point estimation method
Proceedings of the 2011 international symposium on Physical design
A fast heuristic approach for parametric yield enhancement of analog designs
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on verification challenges in the concurrent world
Efficient parametric yield estimation of analog/mixed-signal circuits via Bayesian model fusion
Proceedings of the International Conference on Computer-Aided Design
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In this paper, we propose an efficient numerical algorithm for estimating the parametric yield of analog/RF circuits, considering large-scale process variations. Unlike many traditional approaches that assume normal performance distributions, the proposed approach is particularly developed to handle multiple correlated nonnormal performance distributions, thereby providing better accuracy than the traditional techniques. Starting from a set of quadratic performance models, the proposed parametric yield estimation conceptually maps multiple correlated performance constraints to a single auxiliary constraint by using a MAX operator. As such, the parametric yield is uniquely determined by the probability distribution of the auxiliary constraint and, therefore, can easily be computed. In addition, two novel numerical algorithms are derived from moment matching and statistical Taylor expansion, respectively, to facilitate efficient quadratic statistical MAX approximation. We prove that these two algorithms are mathematically equivalent if the performance distributions are normal. Our numerical examples demonstrate that the proposed algorithm provides an error reduction of 6.5 times compared to a normal-distribution-based method while achieving a runtime speedup of 10-20 times over the Monte Carlo analysis with 103 samples.