Process-Variation Statistical Modeling for VLSI Timing Analysis
Date Issued
2007
Date
2007
Author(s)
Liu, Jui-Hsiang
DOI
en-US
Abstract
As the technology feature sizes are getting smaller than the wave length of optical lithography light source, the process variation issues are also getting significant and must be
taken into consideration during design. Classical corner-based timing analysis produces timing predictions that are often too pessimistic and grossly conservative because we have only few chances to get parameters of all gates working on their corner values. Statistical static timing analysis (SSTA) that characterizes time variables as statistical random variables offers a better approach for more accurate and realistic timing prediction.
Many SSTA algorithms have been proposed and reach maturity. However, most of them were built upon Gaussian distributions due to its simplicity while dealing with maximum operation which is essential during timing analysis. The modeling capability of a signal Gaussian is quite limited and may not be able to deal with various non-Gaussian process distributions.
We use Gaussian polynomial to model the non-Gaussian process variation. However, some high skewness distribution can not be modeled by Gaussian polynomial directly, we may get complex coefficients. Complex coefficients mean the modeling data to be complex number and can not use traditional statistical inference for these datas. Therefore, we
develop a AWE-type statistical moment matching (SMM) method to recover the PDF.
Subjects
製程偏差
統計的模型
靜態時序分析
超大型積體電路
非線性
Process variation
Statistical Modeling
SSTA
VLSI
non-Gaussian
non-linear
Type
thesis
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