International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Fault Detection, Diagnosis, and Optimization of Wafer Manufacturing Processes utilizing Knowledge Creation

  • Bae Hyeon (School of Electrical and Computer Engineering, Pusan National University) ;
  • Kim Sung-Shin (School of Electrical and Computer Engineering, Pusan National University) ;
  • Woo Kwang-Bang (Automation Technology Research Institute, Yonsei University) ;
  • May Gary S. (School of Electrical and Computer Engineering, Georgia Institute of Technology) ;
  • Lee Duk-Kwon (Dept. of Computer System, MEMC Korea Co.)
  • Published : 2006.06.01
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Abstract

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

Keywords

References

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