Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory

  • Pei-Chann Chang
  • Chin Yuan Fan
  • Yen-Wen Wang


Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than 100 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 for further studying of the relationship between the flow time and the various input variables. 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 developed. 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 clustering. Then, a corresponding SGA-CBR method 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.


Due-date assignment Flow time prediction Case-based reasoning Genetic algorithms Self-organizing map 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Information ManagementYuan Ze UniversityTao-YuanTaiwan, R.O.C.
  2. 2.Department of Industrial Engineering & ManagementYuan Ze UniversityTao-YuanTaiwan, R.O.C.
  3. 3.Department of Industrial Engineering & ManagementChing-Yun UniversityTao-YuanTaiwan, R.O.C.

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