Title: A novel design of wafer yield model for semiconductor using a GMDH polynomial and principal component analysis
Authors: Lin, Jun-Shuw
工業工程與管理學系
Department of Industrial Engineering and Management
Keywords: Yield model;Defect cluster index;Group method of data handling (GMDH);Principal component analysis (PCA)
Issue Date: 15-Jun-2012
Abstract: According to previous studies, the Poisson model and negative binomial model could not accurately estimate the wafer yield. Numerous mathematical models proposed in past years were very complicated. Furthermore, other neural networks models can not provide a certain equation for managers to use. Thus, a novel design of this paper is to construct a new wafer yield model with a handy polynomial by using group method of data handling (GMDH). In addition to defect cluster index (CIM), 12 critical electrical test parameters are also considered simultaneously. Because the number of input variables for GMDH is inadvisable to be too many, principal component analysis (PCA) is used to reduce the dimensions of 12 critical electrical test parameters to a manageable few without much loss of information. The proposed approach is validated by a case obtained in a DRAM company in Taiwan. (C) 2011 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2011.09.146
http://hdl.handle.net/11536/15752
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.09.146
Journal: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 39
Issue: 8
Begin Page: 6665
End Page: 6671
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