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Estimation of Defect Clustering Parameter Using Markov Chain Monte Carlo  

Ha, Chung-Hun (School of Information and Computer Engineering, Hongik University)
Chang, Jun-Hyun (School of Information and Computer Engineering, Hongik University)
Kim, Joon-Hyun (School of Information and Computer Engineering, Hongik University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.32, no.3, 2009 , pp. 99-109 More about this Journal
Abstract
Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.
Keywords
Yield Model; Clustering Effect; Parameter Estimation; Markov Chain Monte Carlo; Bayesian Estimation;
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Times Cited By KSCI : 1  (Citation Analysis)
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