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Title:
bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system
Author: Cao Zhengcai ; Deng Jijie ; Liu Min ; Wang Yongji
Keyword: Binary trees ; Data processing ; Dynamical systems ; Forecasting ; Fuzzy systems ; Intelligent control ; Linear transformations ; Scheduling ; Semiconductor device manufacture
Source: Chinese Journal of Chemical Engineering
Issued Date: 2012
Volume: 20, Issue:6, Pages:1081-1088
Indexed Type: EI
Department: (1) College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China; (2) Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China; (3) Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China; (4) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China
Abstract: Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method. © 2012 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).
English Abstract: Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method. © 2012 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).
Language: 英语
WOS ID: WOS:000313776300008
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/15110
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Cao Zhengcai,Deng Jijie,Liu Min,et al. bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system[J]. Chinese Journal of Chemical Engineering,2012-01-01,20(6):1081-1088.
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