Paper Info
Title
Quadratic Statistical Max Approximation For Parametric Yield Estimation Of Analog/Rf Integrated Circuits
Abstract
In this paper, we propose an efficient numerical algorithm for estimating the parametric yield of analog/RF circuits, considering large-scale process variations. Unlike many traditional approaches that assume normal performance distributions, the proposed approach is particularly developed to handle multiple correlated nonnormal performance distributions, thereby providing better accuracy than the traditional techniques. Starting from a set of quadratic performance models, the proposed parametric yield estimation conceptually maps multiple correlated performance constraints to a single auxiliary constraint by using a MAX operator. As such, the parametric yield is uniquely determined by the probability distribution of the auxiliary constraint and, therefore, can easily be computed. In addition, two novel numerical algorithms are derived from moment matching and statistical Taylor expansion, respectively, to facilitate efficient quadratic statistical MAX approximation. We prove that these two algorithms are mathematically equivalent if the performance distributions are normal. Our numerical examples demonstrate that the proposed algorithm provides an error reduction of 6.5 times compared to a normal-distribution-based method while achieving a runtime speedup of 10-20 times over the Monte Carlo analysis with 103 samples.
Year
DOI
Venue
2008
10.1109/TCAD.2008.917582
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
Keywords
Field
DocType
analog/RF circuits, MAX operator, parametric yield
Approximation algorithm,Monte Carlo method,Normal distribution,Quadratic equation,Electronic engineering,Probability distribution,Parametric statistics,Estimation theory,Mathematics,Speedup
Journal
Volume
Issue
ISSN
27
5
0278-0070
Citations 
PageRank 
References 
12
0.88
22
Authors
3
Name
Order
Citations
PageRank
Xin Li126418.95
Yaping Zhan21718.85
Lawrence Pileggi335831.47