Hierarchical statistical characterization of mixed-signal circuits using behavioral modeling
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Proceedings of the 38th annual Design Automation Conference
Remembrance of circuits past: macromodeling by data mining in large analog design spaces
Proceedings of the 39th annual Design Automation Conference
Support vector machines for analog circuit performance representation
Proceedings of the 40th annual Design Automation Conference
Convex Optimization
Proceedings of the 42nd annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model
Proceedings of the 42nd annual Design Automation Conference
Asymptotic probability extraction for non-normal distributions of circuit performance
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Statistical timing analysis under spatial correlations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Worst-case analysis and optimization of VLSI circuit performances
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical performance modeling and optimization
Foundations and Trends in Electronic Design Automation
Statistical modeling with the PSP MOSFET model
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Variability aware SVM macromodel based design centering of analog circuits
Analog Integrated Circuits and Signal Processing
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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(·) 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(·) approximation, facilitating fast yield estimation. Our numerical examples in a commercial BiCMOS process demonstrate that the proposed algorithm provides 2--3x error reduction compared with a Normal-distribution-based method, while achieving orders of magnitude more efficiency than the Monte Carlo analysis with 104 samples.