Enhancing analog yield optimization for variation-aware circuits sizing

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原文链接

NETL

外文摘要：This paper presents a novel approach for improving automated analog yield optimization using a two step exploration strategy。 First， a global optimization phase relies on a modified Lipschitizian optimization to sample the potential optimal sub-regions of the feasible design space。 The search locates a design point near the optimal solution that is used as a starting point by a local optimization phase。 The local search constructs linear interpolating surrogate models of the yield to explore the basin of convergence and to rapidly reach the global optimum。 Experimental results show that our approach locates higher quality design points in terms of yield rate within less run time and without affecting the accuracy。

外文关键词：

OptimizationLinear programmingMathematical modelConvergenceComputational modelingData modelsSearch problems

作者：

Ons Lahiouel、Mohamed H. Zaki、Sofiène Tahar

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作者单位：

Dept. of Electrical and Computer Engineering, Concordia University, Montréal, Québec, Canada

会议名称：

Design, Automation & Test in Europe Conference & Exhibition

会议地点：

Swisstech(CH)

会议母体文献：

Proceedings of the 2017 Design, Automation & Test in Europe

页码：

1273-1276

出版时间：

2017

DOI：

10.23919/DATE.2017.7927187