On Efficient LHS-Based Yield Analysis of Analog Circuits

Authors:
J. Jaffari;M. Anis
Affiliations:
IGNIS Innovation, Inc., Kitchener, ON, Canada;-
Venue:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year:
2011

Citing 0
Cited 2

A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials

ACM Transactions on Design Automation of Electronic Systems (TODAES)
Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits

Proceedings of the 49th Annual Design Automation Conference

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Abstract

The Latin hypercube sampling (LHS) has been used as a variance-reduction estimation tool for an efficient sampling-based variability analysis of analog circuits. For a certain estimation confidence interval, a lower number of LHS samples is needed than that of Monte Carlo due to the estimation variance reduction. In this paper, an analysis of variance decomposition of the indicator function, the yield function, reveals strong contribution of interactive terms in the variance of the yield function, leading to limited performance gain of the traditional LHS sampling. In order to improve its efficiency, two correlation-controlled LHS methods are developed to reduce the required number of LHS samples for analog circuit yield estimation.