bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system

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	bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system
	Cao Zhengcai; Deng Jijie; Liu Min; Wang Yongji
	2012
发表期刊	Chinese Journal of Chemical Engineering
ISSN	1004-9541
卷号	20 期号:6 页码:1081-1088
摘要	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).; 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).
收录类别	EI
关键词	Binary Trees Data Processing Dynamical Systems Forecasting Fuzzy Systems Intelligent Control Linear Transformations Scheduling Semiconductor Device Manufacture
部门归属	(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
语种	英语
WOS记录号	WOS:000313776300008
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15110
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	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,2012,20(6):1081-1088.
APA	Cao Zhengcai,Deng Jijie,Liu Min,&Wang Yongji.(2012).bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system.Chinese Journal of Chemical Engineering,20(6),1081-1088.
MLA	Cao Zhengcai,et al."bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system".Chinese Journal of Chemical Engineering 20.6(2012):1081-1088.