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《中国化学工程学报(英文版)》 2012-06
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Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System

CAO Zhengcai 1,2, ** , DENG Jijie 1 , LIU Min 3 and WANG Yongji 4 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, South- east 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  
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.
【Fund】: Supported by the National Key Basic Research and Development Program of China (2009CB320602);; the National Natural Science Foundation of China (60834004 61025018);; the Open Project Program of the State Key Lab of Industrial ControlTechnology (ICT1108);; the Open Project Program of the State Key Lab of CAD & CG (A1120);; the Foundation of Key Laboratory of System Control and Information Processing (SCIP2011005) Ministry of Education China
【CateGory Index】: TN305
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