The effect of compact modelling strategy on SNM and Read Current variability in Modern SRAM

P. Asenov, F. Adamu-Lema, S. Roy, C. Millar, A. Asenov, G. Roy, U. Kovac, D. Reid
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引用次数: 6

Abstract

It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In this paper we consider two correlated figures of merit for SRAM, Static Noise Margin (SNM) and Read Current. For the purposes of this paper 1,000 3D atomistic simulations of microscopically different 25nm P and N bulk MOSFETs were performed, and statistical compact models were then extracted for each device. Using these models simulations are performed to calculate the SNM and Read Current distributions of SRAM cells constructed using devices from the device ensemble. Variability in device performance has been then introduced via Gaussian or skewed Gaussian threshold voltages (Vt) and by using values of Vt extracted directly from the individual device compact models and the results of these simulations are then compared to the baseline simulations using fully extracted models. The results clearly demonstrate the errors that can be introduced in the estimation of SNM and Read Current distribution of a 6T SRAM cell when statistical device variability is not correctly modelled.
现代SRAM中紧凑建模策略对SNM和读电流变异性的影响
研究表明,100nm以下的SRAM对随机器件变异性特别敏感。本文考虑了SRAM的两个相关指标:静态噪声裕度(SNM)和读电流。为了本文的目的,对微观上不同的25nm P和N块体mosfet进行了1000次三维原子模拟,然后为每个器件提取统计紧凑模型。利用这些模型进行了模拟,计算了使用器件集成中的器件构建的SRAM单元的SNM和读电流分布。然后通过高斯或偏高斯阈值电压(Vt)引入器件性能的可变性,并使用直接从单个器件紧凑模型中提取的Vt值,然后将这些模拟的结果与使用完全提取模型的基线模拟进行比较。结果清楚地表明,当统计器件可变性没有正确建模时,在估计6T SRAM单元的SNM和读电流分布时可能会引入错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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