Numerical estimation of yield in sub-100-nm SRAM design using Monte Carlo simulation

Hyunwoo Nho, Sei Seung Yoon, S. Simon Wong, Seong Ook Jung

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

This paper describes a method to numerically calculate the design margin and to estimate the yield associated with the read access failure for sub-100-nm SRAM. Process variations at sub-100 nm not only affect SRAM cells but also periphery circuits, such as the sense amplifier (SA) and the tracking scheme. Simulation that incorporates both SRAM cells and surrounding circuits is either accurate but computationally expensive (comprehensive Monte Carlo simulation), or overly simple (fixed corner design) and unable to capture crucial statistical variation concern, dominant in sub-100-nm designs. By mathematically combining the separate Monte Carlo simulation results of SRAM cells and each peripheral block, we show that the distribution of the SA input voltage can be estimated accurately in a case where fixed corner simulation underestimates by 19%. We also present the yield equation by combining the SA input voltage and the SA offset distribution, which can be used to choose the design point. In addition, yield sensitivities are derived from the yield data to make sure that the yield has good dependence to design variables.

Original languageEnglish
Pages (from-to)907-911
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume55
Issue number9
DOIs
Publication statusPublished - 2008 Aug 12

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Static random access storage
Networks (circuits)
Electric potential
Monte Carlo simulation

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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abstract = "This paper describes a method to numerically calculate the design margin and to estimate the yield associated with the read access failure for sub-100-nm SRAM. Process variations at sub-100 nm not only affect SRAM cells but also periphery circuits, such as the sense amplifier (SA) and the tracking scheme. Simulation that incorporates both SRAM cells and surrounding circuits is either accurate but computationally expensive (comprehensive Monte Carlo simulation), or overly simple (fixed corner design) and unable to capture crucial statistical variation concern, dominant in sub-100-nm designs. By mathematically combining the separate Monte Carlo simulation results of SRAM cells and each peripheral block, we show that the distribution of the SA input voltage can be estimated accurately in a case where fixed corner simulation underestimates by 19{\%}. We also present the yield equation by combining the SA input voltage and the SA offset distribution, which can be used to choose the design point. In addition, yield sensitivities are derived from the yield data to make sure that the yield has good dependence to design variables.",
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Numerical estimation of yield in sub-100-nm SRAM design using Monte Carlo simulation. / Nho, Hyunwoo; Yoon, Sei Seung; Wong, S. Simon; Jung, Seong Ook.

In: IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 55, No. 9, 12.08.2008, p. 907-911.

Research output: Contribution to journalArticle

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