提出了一种利用非线性幂函数法统计生成非正态分布PSP压缩模型参数的新方法

U. Kovac, D. Dideban, B. Cheng, N. Moezi, G. Roy, A. Asenov
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引用次数: 10

摘要

统计变异性(SV)是制约未来纳米CMOS微缩和集成的基本因素之一。在电路和系统的制造中,可变性感知设计对于实现合理的成品率和可靠性至关重要。为了开发有效的可变性感知设计技术,必须有一个可靠和准确的统计紧凑建模策略。本文提出了一种基于非线性幂方法的统计紧致建模策略。结果表明,与主成分分析(PCA)相比,NPM方法生成的统计紧凑模型参数在捕捉统计参数分布的尾部和非正态分布方面明显更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach to the statistical generation of non-normal distributed PSP compact model parameters using a nonlinear power method
Statistical variability (SV) is one of the fundamental limiting factors for future nano- CMOS scaling and integration of. Variability aware design is essential to achieve reasonable yield and reliability in the manufacture of circuit and systems. To develop effective variability aware design technologies it is essential to have a reliable and accurate statistical compact modeling strategy. In this study a nonlinear power method (NPM) based statistical compact modeling strategy is presented. The results indicate that statistical compact model parameters generated by a NPM approach are significantly better at capturing the tails and non-normal shape of statistical parameter distributions when compared with principal component analysis (PCA).
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