bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system
文献类型：期刊论文
| 作者 | Cao Zhengcai ; Deng Jijie ; Liu Min ; Wang Yongji |
| 刊名 | Chinese Journal of Chemical Engineering
 |
| 出版日期 | 2012 |
| 卷号 | 20期号:6页码:1081-1088 |
| 关键词 | Binary trees Data processing Dynamical systems Forecasting Fuzzy systems Intelligent control Linear transformations Scheduling Semiconductor device manufacture |
| ISSN号 | 1004-9541 |
| 中文摘要 | 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). |
| 英文摘要 | 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). |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000313776300008 |
| 公开日期 | 2013-09-17 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15110]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 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. |
入库方式： OAI收割
来源：软件研究所
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