Process control and management of etching process using data mining with quality indexes

  • Authors:

  • Hyeon Bae;Sungshin Kim;Kwang Bang Woo

  • Affiliations:

  • School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;Automation Technology Research Institute, Yonsei University, Seoul, Korea

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

As argued in this paper, a decision support system based on data mining and knowledge discovery is an important factor in improving productivity and yield. The proposed decision support system consists of a neural network model and an inference system based on fuzzy logic. First, the product results are predicted by the neural network model constructed by the quality index of the products that represent the quality of the etching process. And the quality indexes are classified according to and expert's knowledge. Finally, the product conditions are estimated by the fuzzy inference system using the rules extracted from the classified patterns. We employed data mining and intelligent techniques to find the best condition for the etching process. The proposed decision support system is efficient and easy to be implemented for process management based on an expert's knowledge.