Algorithm for Fast Statistical Timing Analysis

J. Salzmann, F. Sill, D. Timmermann
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引用次数: 8

Abstract

Problems of parameter variations are a main topic in current research and will gain importance in future technology generations due to the continuing scaling. Therefore, it requires appropriate timing analysis which is traditionally done with corner-case simulations. These are quite conservative and pessimistic approaches. In contrast, new statistical static timing analysis (SSTA) algorithms offer a more accurate prediction of the timing behavior of circuit designs. Further, correlations between various parameters and devices can be observed. Unfortunately, the SSTA algorithms mostly require high computational effort and accurate library characterization. This paper proposes an approach for a fast statistical static timing analysis (F-SSTA) with moderate requirements on computation time and library characterization. The approach considers the analysis of gates with multiple inputs. The simulation results show an average error of 5% compared to Monte-Carlo simulations but a significant speed improvement of around 20 times compared to a highly accurate SSTA algorithm.
快速统计时序分析算法
参数变化问题是当前研究的一个主要问题,并且由于规模的不断扩大,在未来的技术世代中将变得越来越重要。因此,需要适当的时序分析,这在传统上是通过拐角情况模拟完成的。这些都是相当保守和悲观的方法。相比之下,新的统计静态时序分析(SSTA)算法可以更准确地预测电路设计的时序行为。此外,可以观察到各种参数和器件之间的相关性。不幸的是,SSTA算法大多需要高计算量和准确的库表征。本文提出了一种对计算时间和库特性要求适中的快速统计静态时序分析(F-SSTA)方法。该方法考虑了多输入门的分析。仿真结果表明,与蒙特卡罗模拟相比,平均误差为5%,但与高精度SSTA算法相比,速度显著提高约20倍。
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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