The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
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
Proceedings of the conference on Design, automation and test in Europe
Breaking the simulation barrier: SRAM evaluation through norm minimization
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the Conference on Design, Automation and Test in Europe
Two fast methods for estimating the minimum standby supply voltage for large SRAMs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient SRAM failure rate prediction via Gibbs sampling
Proceedings of the 48th Design Automation Conference
Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis
Proceedings of the International Conference on Computer-Aided Design
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As the semiconductor technology scales down to 45nm and below, process variations have a profound effect on SRAM cells and an urgent need is to develop fast statistical tools which can accurately estimate the extremely small failure probability of SRAM cells. In this paper, we adopt the Importance Sampling (IS) based information theory inspired Minimum Cross Entropy method, to propose a general technique to quickly evaluate the failure probability of SRAM cells. In particular, we first mathematically formulate the failure of SRAM cells such that the concept of 'Cross Entropy Distance' can be leveraged, and the distance between the ideal distribution for IS and the practical distribution for IS (which is used for generating samples), is well-defined. This cross entropy distance is now minimized resulting in a simple analytical solution to obtain the optimal practical distribution for IS, thereby expediting the convergence of estimation. The experimental results of a commercial 45nm SRAM cell demonstrate that for the same accuracy, the proposed method yields computational savings on the order of 17~50X over the existing state-of-the-art techniques.