FreePDK: An Open-Source Variation-Aware Design Kit
MSE '07 Proceedings of the 2007 IEEE International Conference on Microelectronic Systems Education
Worst-case design and margin for embedded SRAM
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the conference on Design, automation and test in Europe
A methodology for statistical estimation of read access yield in SRAMs
Proceedings of the 45th annual Design Automation Conference
Breaking the simulation barrier: SRAM evaluation through norm minimization
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Introduction to Rare Event Simulation
Introduction to Rare Event Simulation
Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis
Proceedings of the International Conference on Computer-Aided Design
A fast estimation of SRAM failure rate using probability collectives
Proceedings of the 2012 ACM international symposium on International Symposium on Physical Design
Cross entropy minimization for efficient estimation of SRAM failure rate
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Proceedings of the International Conference on Computer-Aided Design
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The impact of process variation in deep-submicron technologies is especially pronounced for SRAM architectures which must meet demands for higher density and higher performance at increased levels of integration. Due to the complex structure of SRAM, estimating the effect of process variation accurately has become very challenging. In this paper, we address this challenge in the context of estimating SRAM timing variation. Specifically, we introduce a method called loop flattening that demonstrates how the evaluation of the timing statistics in the complex, highly structured circuit can be reduced to that of a single chain of component circuits. To then very quickly evaluate the timing delay of a single chain, we employ a statistical method based on importance sampling augmented with targeted, high-dimensional, spherical sampling. Overall, our methodology provides an accurate estimation with 650X or greater speed-up over the nominal Monte Carlo approach.