Combining SOM and GA-CBR for flow time prediction in semiconductor manufacturing factory

Authors:
Pei-Chann Chang;Yen-Wen Wang;Chen-Hao Liu
Affiliations:
Department of Information Management, Yuan-Ze University;Department of Industrial Engineering and Management, Ching-Yun University, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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Abstract

Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than hundred of production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is proposed. And Genetic Algorithm (GA) is applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding GA-CBR is selected and applied for flow time prediction. Finally, using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches.