Fuzzy-neural approaches with example post-classification for estimating job cycle time in a wafer fab

  • Authors:

  • Toly Chen;Hsin-Chieh Wu;Yi-Chi Wang

  • Affiliations:

  • Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 407, Taiwan, ROC;Department of Industrial Engineering and Management, Chaoyang University of Technology, Taiwan, ROC;Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 407, Taiwan, ROC

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2009

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Abstract

Estimating the cycle time of a job in a wafer fabrication plant (wafer fab) is a critical task to the wafer fab. Many recent studies have shown that pre-classifying a job before estimating the cycle time was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, two hybrid approaches with example post-classification, the equally-divided method and the proportional-to-error method, are proposed in this study in which a job is post-classified by a back propagation network (BPN) instead after the forecasting error is generated. In this novel way, only jobs whose cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation (PS) is applied in this study to generate test data. According to experimental results, the forecasting accuracy (measured with root mean squared error, RMSE) of the proportional-to-error method was significantly better than those of the other approaches in most cases by achieving a 26-56% (and an average of 41%) reduction in RMSE over the comparison basis - multiple-factor linear combination (MFLC). The effect of post-classification was also statistically significant.