Worst Case Sampling Method with Confidence Ellipse for Estimating the Impact of Random Variation on Static Random Access Memory (SRAM)

다국어 초록 (Multilingual Abstract)

As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a novel sampling method based on the concept of a confidence ellipse. Results show that the proposed method estimates the SRAM margin metrics in highsigma regimes more efficiently than the standard Monte Carlo (MC) method.

목차 (Table of Contents)

• Abstract
• Ⅰ. INTRODUCTION
• Ⅱ. THEORETICAL BACKGROUND
• Ⅲ. SAMPLING METHOD WITH CONFIDENCE ELLIPSE AND HYPERBOLA
• Ⅳ. APPLICATION TO CACHE MEMORY : CASE STUDY
• Ⅴ. CONCLUSIONS
• REFERENCES

참고문헌 (Reference)

1 G. S. Lee, "Worst case sampling method to estimate the impact of random variation on static random access memory" 2015

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3 H. Thomas, "Statistics: methods and applications: a comprehensive reference for science, industry, and data mining" StatSoft, Inc 2006

4 R. Kanj, "Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events" 69-72, 2006

5 Z. Guo, "Large-scale SRAM variability characterization in 45 nm CMOS" 44 (44): 3174-3192, 2009

6 G. Box, "An analysis of transformations" 26 (26): 211-252, 1964