Statistical performance modeling and optimization
Foundations and Trends in Electronic Design Automation
Adaptive post-silicon tuning for analog circuits: concept, analysis and optimization
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Distribution arithmetic for stochastical analysis
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Proceedings of the 45th annual Design Automation Conference
A general method to evaluate RF BIST techniques based on non-parametric density estimation
Proceedings of the conference on Design, automation and test in Europe
A Gaussian mixture model for statistical timing analysis
Proceedings of the 46th Annual Design Automation Conference
Yield-driven iterative robust circuit optimization algorithm
Proceedings of the 46th Annual Design Automation Conference
Fast and accurate statistical criticality computation under process variations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Evaluation of analog/RF test measurements at the design stage
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fast, non-Monte-Carlo estimation of transient performance variation due to device mismatch
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Practical Monte-Carlo based timing yield estimation of digital circuits
Proceedings of the Conference on Design, Automation and Test in Europe
Advanced variance reduction and sampling techniques for efficient statistical timing analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
The truncated Stieltjes moment problem solved by using kernel density functions
Journal of Computational and Applied Mathematics
Efficient parametric yield estimation of analog/mixed-signal circuits via Bayesian model fusion
Proceedings of the International Conference on Computer-Aided Design
Proceedings of the 50th Annual Design Automation Conference
Proceedings of the International Conference on Computer-Aided Design
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While process variations are becoming more significant with each new IC technology generation, they are often modeled via linear regression models so that the resulting performance variations can be captured via normal distributions. Nonlinear response surface models (e.g., quadratic polynomials) can be utilized to capture larger scale process variations; however, such models result in nonnormal distributions for circuit performance. These performance distributions are difficult to capture efficiently since the distribution model is unknown. In this paper, an asymptotic-probability-extraction (APEX) method for estimating the unknown random distribution when using a nonlinear response surface modeling is proposed. The APEX begins by efficiently computing the high-order moments of the unknown distribution and then applies moment matching to approximate the characteristic function of the random distribution by an efficient rational function. It is proven that such a moment-matching approach is asymptotically convergent when applied to quadratic response surface models. In addition, a number of novel algorithms and methods, including binomial moment evaluation, PDF/CDF shifting, nonlinear companding and reverse evaluation, are proposed to improve the computation efficiency and/or approximation accuracy. Several circuit examples from both digital and analog applications demonstrate that APEX can provide better accuracy than a Monte Carlo simulation with 104 samples and achieve up to 10times more efficiency. The error, incurred by the popular normal modeling assumption for several circuit examples designed in standard IC technologies, is also shown