DocumentCode
1851715
Title
On-Line Rescheduling for Semiconductor Manufacturing
Author
Huang, Han-Pang ; Chen, Tien-Ying
Author_Institution
Dept. of Mech. Eng., Nat. Taiwan Univ., Taipei
fYear
2006
fDate
8-10 Oct. 2006
Firstpage
106
Lastpage
111
Abstract
Semiconductor wafer fabrication involves one of the most complex manufacturing processes ever used. To control such complex systems, it is a challenge to determine appropriate dispatching strategies under various system conditions. Dispatching strategies are crucial for the system performance, especially for the real time control of the system. In this paper, an interval variant rescheduling mechanism is proposed. In order to deploy different dispatching rules to different intrabays, k-means is used for clustering the intrabays of the fab. Then genetic algorithm (GA) is used for searching dispatching rule sets which promote better performance. In terms of the system conditions corresponding to dispatching rules, the support vector machine (SVM) classifier is constructed as the scheduler. In addition, the adaptive neuro-fuzzy inference system (ANFIS) prediction model is built for the sake of on-line deciding the scheduling intervals. The results indicate that applying the proposed mechanism to obtaining dispatching strategies is an effective method considering the complexity and variation of semiconductor wafer fabrication systems
Keywords
adaptive scheduling; fuzzy reasoning; genetic algorithms; pattern classification; production control; semiconductor device manufacture; support vector machines; ANFIS prediction model; SVM classifier; adaptive neuro-fuzzy inference system; dispatching strategies; fab intrabay clustering; genetic algorithms; interval variant rescheduling mechanism; k-means clustering; online rescheduling; real time control; semiconductor wafer fabrication; support vector machine classifier; Control systems; Dispatching; Fabrication; Job shop scheduling; Manufacturing processes; Real time systems; Semiconductor device manufacture; Support vector machine classification; Support vector machines; System performance;
fLanguage
English
Publisher
ieee
Conference_Titel
Automation Science and Engineering, 2006. CASE '06. IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
1-4244-0310-3
Electronic_ISBN
1-4244-0311-1
Type
conf
DOI
10.1109/COASE.2006.326863
Filename
4120329
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