Parameterized block-based non-gaussian statistical gate timing analysis

Abbaspour, Soroush; Fatemi, Hanif; Pedram, Massoud

doi:10.1145/1118299.1118512

Cited by 4 publications

References 15 publications

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Non-Gaussian Statistical Interconnect Timing Analysis

Abbaspour

Fatemi

Pedram

2006

Proceedings of the Design Automation &Amp; Test in Europe Conference

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This paper focuses on statistical interconnect timing analysis in a parameterized block-based statistical static timing analysis tool. In particular, a new framework for performing timing analysis of RLC networks with step inputs, under both Gaussian and non-Gaussian sources of variation, is presented. In this framework, resistance, inductance, and capacitance of the RLC line are modeled in a canonical first order form and used to produce the corresponding propagation delay and slew (time) in the canonical first-order form. To accomplish this step, mean, variance, and skewness of delay and slew distributions are obtained in an efficient, yet accurate, manner. The proposed framework can be extended to consider higher order terms of the various sources of variation. Experimental results show average errors of less than 2% for the mean, variance and skewness of interconnect delay and slew while achieving orders of magnitude speedup with respect to a Monte Carlo simulation with 10 4 samples.

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Non-Gaussian Statistical Interconnect Timing Analysis

Abbaspour

Fatemi

Pedram

2006

Proceedings of the Design Automation &Amp; Test in Europe Conference

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Statistical Timing Analysis: From Basic Principles to State of the Art

Blaauw

Chopra

Srivastava

et al. 2008

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

310

171

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Non-Gaussian Statistical Timing Analysis Using Second-Order Polynomial Fitting

Cheng

Xiong

2009

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In the nanometer manufacturing region, process variation causes significant uncertainty for circuit performance verification. Statistical static timing analysis (SSTA) is thus developed to estimate timing distribution under process variation. However, most of the existing SSTA techniques have difficulty in handling the non-Gaussian variation distribution and non-linear dependency of delay on variation sources. To solve such a problem, in this paper, we first propose a new method to approximate the max operation of two nonGaussian random variables through second-order polynomial fitting. We then present new non-Gaussian SSTA algorithms under two types of variational delay models: quadratic model and semi-quadratic model (i.e., quadratic model without crossing terms). All atomic operations (such as max and sum) of our algorithms are performed by closed-form formulas, hence they scale well for large designs. Experimental results show that compared to the Monte-Carlo simulation, our approach predicts the mean, standard deviation, and skewness within 1%, 1%, and 5% error, respectively. Our approach is more accurate and also 20x faster than the most recent method for non-Gaussian and nonlinear SSTA.

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Parameterized block-based non-gaussian statistical gate timing analysis

Abstract: Abstract

Cited by 4 publications

References 15 publications

Non-Gaussian Statistical Interconnect Timing Analysis

Non-Gaussian Statistical Interconnect Timing Analysis

Statistical Timing Analysis: From Basic Principles to State of the Art

Non-Gaussian Statistical Timing Analysis Using Second-Order Polynomial Fitting

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