Paper Info
Title
Efficient Bayesian Yield Optimization Approach for Analog and SRAM Circuits.
Abstract
Conventional yield optimization approaches rely on accurate yield estimation for given design parameters, which would be computational intensive. In this paper, a novel Bayesian yield optimization approach is proposed for analog and SRAM circuits. An equivalent a problem is formulated via applying Bayes' theorem on the augmented yield problem. The yield optimization problem is converted to identifying the design parameters with maximal probability density conditioning on the event that the corresponding circuit is \"pass\". Gaussian kernel density estimation is employed to approximate the conditional probability, and a multi-start-point based EM-like algorithm is proposed to solve the equivalent problem efficiently Compared with the state-of-the-art yield optimization approaches, the proposed method can significantly reduce the number of circuit simulations with comparable optimization accuracy by avoiding repetitive yield estimations.
Year
DOI
Venue
2017
10.1145/3061639.3062234
DAC
Keywords
Field
DocType
conditional probability,analog circuits,multistart-point based EM-like algorithm,gaussian kernel density estimation,corresponding circuit,maximal probability density conditioning,yield optimization problem,augmented yield problem,equivalent problem,novel Bayesian yield optimization approach,accurate yield estimation,SRAM circuits,efficient Bayesian yield optimization approach,repetitive yield estimations,comparable optimization accuracy,circuit simulations
Kernel (linear algebra),Density estimation,Mathematical optimization,Conditional probability,Computer science,Probability density function,Gaussian function,Optimization problem,Bayesian probability,Bayes' theorem
Conference
ISSN
ISBN
Citations 
0738-100X
978-1-5090-5664-4
0
PageRank 
References 
Authors
0.34
12
5
Name
Order
Citations
PageRank
Mengshuo Wang1212.23
Fan Yang2256.98
Changhao Yan3276.64
Xuan Zeng440875.96
Xiang-Dong Hu533.09