Control variates for quantile estimation
Management Science
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
The effective dimension and quasi-Monte Carlo integration
Journal of Complexity
Simulation, Fourth Edition
Efficient Monte Carlo based incremental statistical timing analysis
Proceedings of the 45th annual Design Automation Conference
Practical, fast Monte Carlo statistical static timing analysis: why and how
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
On efficient Monte Carlo-based statistical static timing analysis of digital circuits
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Timing yield estimation of digital circuits using a control variate technique
ISQED '09 Proceedings of the 2009 10th International Symposium on Quality of Electronic Design
Statistical timing analysis under spatial correlations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Asymptotic Probability Extraction for Nonnormal Performance Distributions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical Timing Analysis: From Basic Principles to State of the Art
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
An efficient control variates method for yield estimation of analog circuits based on a local model
Proceedings of the International Conference on Computer-Aided Design
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The advanced sampling and variance reduction techniques as efficient alternatives to the slow crude-MC method have recently been adopted for the analysis of timing yield in digital circuits. However, these techniques, the Quasi-MC method and the order-statistics base estimator, are prone to bias or negligible improvement upon the crude-MC method when an early-stage timing analysis with few (10s) simulation iterations can be afforded. In this paper, these issues are studied and a control variate-base technique is developed to accurately estimate the moments of circuits' critical delays with very few timing simulation iterations. A skew-normal distribution is then used to form a closed-form cumulative distribution function of timing yield. Analysis of the benchmark circuits shows 3--10X reduction of the confidence interval ranges of the estimated yield compared to the crude-MC translating to 9--100X reduction in the number of samples for the same analysis accuracy.