Browsing by Author "Lin, Jun-Shuw"

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A novel design of wafer yield model for semiconductor using a GMDH polynomial and principal component analysis
(2012-06-15)
Lin, Jun-Shuw
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
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.
A systematic estimation model for fraction nonconforming of a wafer in semiconductor manufacturing research
(2012-06-01)
Lin, Jun-Shuw
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The clustering phenomenon of defects usually occurs in semiconductor manufacturing. However, previous studies did not pay much attention to the influence of clustering phenomenon for estimating fraction nonconforming of a wafer. Thus, this paper presents a systematic estimation model with considering relevant variables about clustering defects for fraction nonconforming of a wafer. The method combines back-propagation neural network (BPNN) with genetic algorithm (GA) to obtain an estimation model. In this study, GA aims to optimize the parameters of BPNN. Five relevant variables: number of defects (ND), squared coefficient of angle variation (SCVA) for defects, squared coefficient of distance variation (SCVD) for defects, defect cluster index (CIM), and the number of cluster groups (NCG) for defects by self-organized map (SOM) are utilized as inputs for GA-BPNN. Finally, a simulation case and a real-world case are used to confirm the effectiveness of proposed method. (C) 2012 Elsevier B. V. All rights reserved.
Constructing a yield model for integrated circuits based on a novel fuzzy variable of clustered defect pattern
(2012-02-15)
Lin, Jun-Shuw
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
As the wafer size increases, the clustering phenomenon of defects becomes significant. In addition to clustered defects, various clustering patterns also influence the wafer yield. In fact, the recognition of clustering pattern usually exists fuzziness. However, the wafer yield models in previous studies did not consider the fuzziness of clustering pattern belonging to which shape in recognition. Therefore, the objective of this study is to develop a new fuzzy variable of clustering pattern (FVCP) by using fuzzy logic control, and predict the wafer yield by using back-propagation neural network (BPNN) incorporating ant colony optimization (ACO). The proposed method utilizes defect counts, cluster index (CI), and FVCP as inputs for ACO-BPNN. A simulated study is utilized to demonstrate the effectiveness of the proposed model. (C) 2011 Elsevier Ltd. All rights reserved.
Constructing a yield model for integrated circuits based on a novel fuzzy variable of clustered defect pattern
(2011-02-15)
Lin, Jun-Shuw
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
As the wafer size increases, the clustering phenomenon of defects becomes significant. In addition to clustered defects, various clustering patterns also influence the wafer yield. In fact, the recognition of clustering pattern usually exists fuzziness. However, the wafer yield models in previous studies did not consider the fuzziness of clustering pattern belonging to which shape in recognition. Therefore, the objective of this study is to develop a new fuzzy variable of clustering pattern (FVCP) by using fuzzy logic control, and predict the wafer yield by using back-propagation neural network (BPNN) incorporating ant colony optimization (ACO). The proposed method utilizes defect counts, cluster index (CI), and FVCP as inputs for ACO-BPNN. A simulated study is utilized to demonstrate the effectiveness of the proposed model. (C) 2011 Elsevier Ltd. All rights reserved.