Active neural network control of wafer attributes in a plasma et

Details Active neural network control of wafer attributes in a plasma et Active neural network control of wafer attributes in a plasma et

1995-06-06

1998-04-07

Davis, George B.

395 24, 395903, 395906, G06F 1518

Patent

active

057374961

ABSTRACT:
The present invention is predicated upon the fact that an emission trace from a plasma glow used in fabricating integrated circuits contains information about phenoma which cause variations in the fabrication process such as age of the plasma reactor, densities of the wafers exposed to the plasma, chemistry of the plasma, and concentration of the remaining material. In accordance with the present invention, a method for using neural networks to determine plasma etch end-point times in an integrated circuit fabrication process is disclosed. The end-point time is based on in-situ monitoring of the optical emission trace. The back-propagation method is used to train the network. More generally, a neural network can be used to regulate control variables and materials in a manufacturing process to yield an output product with desired quality attributes. An identified process signature which reflects the relation between the quality attribute and the process may be used to train the neural network.

REFERENCES:
patent: 5052043 (1991-09-01), Gaborski
patent: 5056037 (1991-10-01), Eberhardt
patent: 5107454 (1992-04-01), Niki
patent: 5114233 (1992-05-01), Clark et al.
patent: 5119468 (1992-06-01), Owens
patent: 5129039 (1992-07-01), Hiraiwa
patent: 5175678 (1992-12-01), Frerichs et al.
patent: 5259064 (1993-11-01), Furuta et al.
patent: 5286947 (1994-02-01), Clyde et al.
patent: 5297237 (1994-03-01), Masuoka et al.
patent: 5408588 (1995-04-01), Ulug
patent: 5479575 (1995-12-01), Yoda
patent: 5559690 (1996-09-01), Keeler et al.
C. D. Himmel et al., "A Comparison of Statistically-Based and Neural Network Models of Plasma Etch Behavior," IEEE/SEMI Intl. Semiconductor Manufacturing Science Symposium, 124-129 (Jun. 15-16, 1992).
S-S. Chen, "Intelligent Control of Semiconductor Manufacturing Processes," IEEE Int'l Conference on Fuzzy Systems, 101-108 (Mar. 8-12, 1992).
R. Shadmehr et al., "Principal Component Analysis of Optical Emission Spectroscopy and Mass Spectrometry: Application to Reactive Ion Etch Process Parameter Estimation Using Neural Networks," J. Electrochem. Soc., vol. 139, No. 3, 907-914 (Mar. 1992).
E.A. Rietman et al., "Active Neural Network Control of Wafer Attributes in a Plasma Etch Process," J. Vac. Sci. Technol. B vol. 11, No. 4, 1314-1316 (Jul./Aug. 1993).
"Neural Networks for Control," Edited by W. Thomas Miller, III, Richard S. Sutton, and Paul J. Werbos, A Bradford Book, MIT Press (1990).
S. M. Weiss et al., "Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and expert Systems," 12, 13, 54-68, Morgan Kaufmann Publishers, Inc. (1991).
Panel on Plasma Processing of Materials, "Plasma Processing of Materials: Scientific Opportunities and Technological Challenges," 6, 13-36, National Academy Press, Washington, DC (1991).
R. G. Poulsen, "Plasma Etching in Integrated Circuit Manufacture -A Review*," J. Vac. Sci. Technol., 266-274, vol. 14, No. 1 (Jan./Feb. 1977).
J. Denker et al., "Large Automatic Learning, Rule Extraction and Generalization," Complex Systems 1, 877-922 (1987).
B. Widrow et al., "30 Years of Adaptive Neural Networks: Perception, Madaline, and Backpropagation," Proceedings of the IEEE, 1415-1442, vol. 78, No. 9 (Sep. 1990).
D.E. Rumelhart et al., "Parallel Distributed Processing: Explorations in the Microstructure of Cognition (vol. 1;Foundatins)," (A Bradford Book), 318-330, The MIT Press (1986).
E.A. Rietman et al., "Neural Networks That Learn," Algorithm -The Personal Programming Newsletter, 17-21 (May/Jun. 1990).
K. Hornik et al., "Universal Approximation of an Unknown Mapping and Its Derivatives using Multilayer Feedforward Networks," Neural Networks, 551-560, vol. 3 (1990).
R.R. Bowman et al., "Practical Integrated Circuit Fabrication," Section 9-Dry Etching, Integrated Circuit Engineering Corporation, 9-1 through 9-10 (1984).
A.K. Dewdney, "Computer Recreations," Algorithm, 3,4, 11-15 (Oct. 1992).
K.D. Schnelle et al., "A Real-Time Expert System for Quality Control," IEEE Expert, 36-42 (1992).
C. Barnes et al., "Applications of Neural Networks to Process Control and Modeling," Artificial Neural Networks, 321-326, vol. 1 (Jun. 1991).
Frye et al, "Back-propagation Learning and Nonidealities in Analog Neural Network Hardware", IEEE Transactions on Neural Networks, vol. 2, No. 1, Jan. 1991.
Rietman et al, "Neural Network Control of a Plasma Gate Etch: Early Stepsin Wafer-To Wafer Process Control", Fifteen IEEE/CHMT International Electronics Manufacturing Technology Symposium, 4-6 Oct. 1993.

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