DocumentCode
1442804
Title
A CVD epitaxial deposition in a vertical barrel reactor: process modeling using cluster-based fuzzy logic models
Author
Chiou, J.C. ; Yang, J.Y.
Author_Institution
Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Volume
11
Issue
4
fYear
1998
fDate
11/1/1998 12:00:00 AM
Firstpage
645
Lastpage
653
Abstract
A chemical vapor deposition (CVD) epitaxial deposition process modeling using fuzzy logic models (FLM´s) has been proposed. The process modeling algorithm consists of a cluster estimation method and backpropagation algorithm to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation factor is used to obtain the optimum structure of the fuzzy model using the testing data. Upon the optimum structure being reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to a nonlinear function and a vertical chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models
Keywords
backpropagation; estimation theory; fuzzy logic; identification; semiconductor process modelling; vapour phase epitaxial growth; CVD epitaxial deposition; backpropagation algorithm; chemical vapor deposition; cluster estimation method; cluster-based fuzzy logic models; decision rule; gradient-descent method; multiple correlation factor; nonlinear function; process modeling; training data; vertical CVD process; vertical barrel reactor; Backpropagation algorithms; Chemical vapor deposition; Clustering algorithms; Fuzzy logic; Inductors; Input variables; Manufacturing processes; Process control; Semiconductor process modeling; Testing;
fLanguage
English
Journal_Title
Semiconductor Manufacturing, IEEE Transactions on
Publisher
ieee
ISSN
0894-6507
Type
jour
DOI
10.1109/66.728562
Filename
728562
Link To Document