Fundamentals of modern VLSI devices
Fundamentals of modern VLSI devices
Parallel Importance Separation and Adaptive Monte Carlo Algorithms for Multiple Integrals
NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
Design for Variability in DSM Technologies
ISQED '00 Proceedings of the 1st International Symposium on Quality of Electronic Design
Statistical design and optimization of SRAM cell for yield enhancement
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Variability in sub-100nm SRAM designs
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Statistical analysis of SRAM cell stability
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Efficient Monte Carlo based incremental statistical timing analysis
Proceedings of the 45th annual Design Automation Conference
On efficient Monte Carlo-based statistical static timing analysis of digital circuits
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Quick simulation: a review of importance sampling techniques in communications systems
IEEE Journal on Selected Areas in Communications
Correlation controlled sampling for efficient variability analysis of analog circuits
Proceedings of the Conference on Design, Automation and Test in Europe
Efficient SRAM failure rate prediction via Gibbs sampling
Proceedings of the 48th Design Automation Conference
Rethinking memory redundancy: optimal bit cell repair for maximum-information storage
Proceedings of the 48th Design Automation Conference
A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Maximum-information storage system: concept, implementation and application
Proceedings of the International Conference on Computer-Aided Design
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In this paper, an adaptive sampling method is proposed for the statistical SRAM cell analysis. The method is composed of two components. One part is the adaptive sampler that manipulates an alternative sampling distribution iteratively to minimize the estimated yield error. The drifts of the sampling distribution are re-configured in each iteration toward further minimization of the estimation variance by using the data obtained from the previous circuit simulations and applying a high-order Householder's method. Secondly, an analytical framework is developed and integrated with the adaptive sampler to further boost the efficiency of the method. This is achieved by the optimal initialization of the alternative multi-variate Gaussian distribution via setting its drift vector and covariance matrix. The required number of simulation iterations to obtain the yield with a certain accuracy is several orders of magnitude lower than that of the crude-Monte Carlo method with the same confidence interval.