Fast statistical analysis of nonlinear analog circuits using model order reduction

被引：0
作者：
Henda Aridhi
Mohamed H. Zaki
Sofiène Tahar
机构：
[1] Concordia University,Department of Electrical and Computer Engineering
来源：
Analog Integrated Circuits and Signal Processing | 2015年 / 85卷
关键词：
Clustering; Monte Carlo; Model order reduction ; Nonlinear analog circuits; Perturbation theory; Projection; Statistical simulation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The reduction of the computational cost of statistical circuit analysis, such as Monte Carlo (MC) simulation, is a challenging problem. In this paper, we propose to build macromodels capable of reproducing the statistical behavior of all repeated MC simulations in a single simulation run. The parameter space is sampled similarly to the MC method and the resulting nonlinear models are reduced simultaneously to a small macromodel using nonlinear model order reduction method based on projection, perturbation theory and linearization techniques. We demonstrate the effectiveness of the proposed method for three applications: a current mirror, an operational transconductance amplifier, and a three inverter chain under the effect of current factor and threshold voltage variations. Our experimental results show that our method provides a speedup in the range 100–500 over 1000 samples of MC simulation.
引用
收藏
页码：379 / 394
页数：15
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