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

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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 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.

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Authors and Affiliations

  1. Department of Information Management, Yuan Ze University, No. 135,Yuan-Tung Rd., Chung-li, Tao-Yuan, 32026, Taiwan, R.O.C.

    Pei-Chann Chang

  2. Department of Industrial Engineering & Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-li, Tao-Yuan, 32026, Taiwan, R.O.C.

    Chin Yuan Fan

  3. Department of Industrial Engineering & Management, Ching-Yun University, Tao-Yuan, 32026, Taiwan, R.O.C.

    Yen-Wen Wang

Authors
  1. Pei-Chann Chang
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  2. Chin Yuan Fan
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  3. Yen-Wen Wang
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Correspondence to Pei-Chann Chang.

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Chang, PC., Fan, C.Y. & Wang, YW. Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory. J Intell Manuf 20, 421–429 (2009). https://doi.org/10.1007/s10845-008-0116-2

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  • DOI: https://doi.org/10.1007/s10845-008-0116-2

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