JSTS:Journal of Semiconductor Technology and Science

  • 제15권3호
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  • Pages.374-380
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  • 2015
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  • 1598-1657(pISSN)
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  • 2233-4866(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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Worst Case Sampling Method with Confidence Ellipse for Estimating the Impact of Random Variation on Static Random Access Memory (SRAM)

  • Oh, Sangheon (School of Electrical and Computer Engineering, University of Seoul) ;
  • Jo, Jaesung (School of Electrical and Computer Engineering, University of Seoul) ;
  • Lee, Hyunjae (School of Electrical and Computer Engineering, University of Seoul) ;
  • Lee, Gyo Sub (School of Electrical and Computer Engineering, University of Seoul) ;
  • Park, Jung-Dong (Division of Electronics and Electrical Engineering, Dongguk University) ;
  • Shin, Changhwan (School of Electrical and Computer Engineering, University of Seoul)
  • 투고 : 2015.01.10
  • 심사 : 2015.04.05
  • 발행 : 2015.06.30
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초록

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 high-sigma regimes more efficiently than the standard Monte Carlo (MC) method.

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참고문헌

  1. K. Agarwal and S. Nassif, "The impact of random device variation on SRAM cell stability in sub-90- nm CMOS technologies," in IEEE Trans. VLSI Syst., vol. 16, no. 1, pp. 86 -97, Jan. 2008. https://doi.org/10.1109/TVLSI.2007.909792
  2. R. Kanj, R. Joshi and S. Nassif, "Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events," Proc. DAC, pp. 69 -72, 2006.
  3. G. S. Lee and C. Shin, "Worst case sampling method to estimate the impact of random variation on static random access memory," in IEEE Trans. Electron Devices, Jun. 2015 (in press).
  4. Z. Guo, A. Carlson, L. T. Pang, K. T. Duong, T.- J. King Liu, and B. Nikolic, "Large-scale SRAM variability characterization in 45 nm CMOS," in IEEE J. Solid-State Circuits, vol. 44, no. 11, pp. 3174-3192, Nov. 2009. https://doi.org/10.1109/JSSC.2009.2032698
  5. H. Thomas and P. Lewicki, Statistics: methods and applications: a comprehensive reference for science, industry, and data mining, StatSoft, Inc., 2006.
  6. G. Box and D. Cox, "An analysis of transformations," in J. Royal Statistical Soc. Series B (Methodological), vol. 26, no. 2, pp. 211-252, 1964.