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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Liebowitz, J. (ed.): Knowledge Management Handbook. CRC Press, MA (1999)
Berry, M.J.A., Linoff, G.S.: Data Mining Techniques. John Wiley & Sons Inc., Canada (1997)
Nadine, T.G.: The neural network model RuleNet and its application to mobile robot navigation. Fuzzy Sets and Systems 85, 287–303 (1997)
Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270 (1997)
Lin, C.T.: Neural Fuzzy Control Systems with Structure and Parameter Learning. World Scientific Pub. Co., New York (1994)
Omatu, S., Khalid, M., Yusof, R.: Neuro-Control and its Applications. Springer, Heidelberg (1995)
Shi, Y., Mizumoto, M.: An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules. Fuzzy Sets and Systems 118, 339–350 (2001)
Wang, X.Z., Wang, Y.D., Xu, X.F., Ling, W.D., Yeung, D.S.: A new approach to fuzzy rule generation-fuzzy extension matrix. Fuzzy Sets and Systems 123, 291–306 (2001)
Abe, S., Lan, M.S.: A Function Approximator Using Fuzzy Rules Extracted Directly From Numerical Data. In: Proceedings of 1993 International Joint Conference on Neural Networks, vol. 2, pp. 1887–1892 (1993)
Pal, N.R., Chakraborty, S.: Fuzzy rule extraction from ID3-type decision trees for real data. IEEE Transactions on Systems, Man and Cybernetics, Part B 31, 745–754 (2001)
Roy, R.K.: A Primer on the Taguchi Method. Society of Manufacturing Engineers (1990)
Tsoukalas, L.H., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering. John Wiley & Sons Inc., New York (1997)
Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, NZ (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bae, H., Kim, S., Woo, K.B. (2005). Process Control and Management of Etching Process Using Data Mining with Quality Indexes. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_154
Download citation
DOI: https://doi.org/10.1007/11539087_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)