Chapter

Rough Sets and Current Trends in Computing

Volume 4259 of the series Lecture Notes in Computer Science pp 767-775

Combining SOM and GA-CBR for Flow Time Prediction in Semiconductor Manufacturing Factory

  • Pei-Chann ChangAffiliated withDepartment of Information Management, Yuan-Ze UniversityDepartment of Industrial Engineering and Management, Yuan-Ze University
  • , Yen-Wen WangAffiliated withDepartment of Industrial Engineering and Management, Ching-Yun University
  • , Chen-Hao LiuAffiliated withDepartment of Industrial Engineering and Management, Yuan-Ze University

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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.

Keywords

Flow time prediction Case-Based Reasoning Genetic Algorithms Self-Organizing Map