Abstract
The principal object of this paper is to develop a neural network model, which can simulate the plasma enhanced chemical vapor deposition (PECVD) process in TFT-Array procedure. Then the Boolean logic rules are extracted from the trained neural network in order to establish a knowledge base of expert system. The input data of neural network was collected form the process parameters of PECVD machines in the TFT-Array department, included the flow rate of all gases, pressure and temperature of the chamber, etc. After checking, explaining and integrating the extraction rules into knowledge base, the rules can be the basics of membrane thickness prediction and alarm diagnosis in PECVD system.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, M., Chen, JC., Heh, JS. (2006). The Control of Membrane Thickness in PECVD Process Utilizing a Rule Extraction Technique of Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_159
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DOI: https://doi.org/10.1007/11760191_159
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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