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Fast statistical analysis of nonlinear analog circuits using model order reduction

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Abstract

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.

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  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Henda Aridhi, Mohamed H. Zaki & Sofiène Tahar

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  1. Henda Aridhi
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Correspondence to Henda Aridhi.

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Aridhi, H., Zaki, M.H. & Tahar, S. Fast statistical analysis of nonlinear analog circuits using model order reduction. Analog Integr Circ Sig Process 85, 379–394 (2015). https://doi.org/10.1007/s10470-015-0588-x

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