Reactive ion etch modeling using neural networks and simulated annealing

IEEE transactions on components, packaging and manufacturing technology. Part C. Manufacturing

Volumn 19, Issue 1, 1996, Pages 3-8

  Kim, Byungwhan ^a   May, Gary S ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; DIELECTRIC MATERIALS; INTEGRATED CIRCUITS; LEARNING SYSTEMS; MULTICHIP MODULES; NEURAL NETWORKS; PLASMAS; SILICA; SIMULATED ANNEALING;

GENERALIZED DELTA RULE; NETWORK MEMORY;

REACTIVE ION ETCHING;

EID: 0029777175     PISSN: 10834400     EISSN: None     Source Type: Journal    
DOI: 10.1109/3476.484199     Document Type: Article

References (19)

1. M. Ceiler S. Bidstrup P. Kohl G. May The effect of deposition conditions on the properties of pecvd silicon dioxide films 2nd Int. Symp. Electrochemical Processing of Tailored Materials 2nd Int. Symp. Electrochemical Processing of Tailored Materials Honolulu HI 1993-May
2. A. Gerodolle J. Pelletier Two-dimensional implications of a purely reactive model for plasma etching IEEE Trans. Elec. Dev. 38 9 2025 2032 Sept. 1991 16 2732 83725
3. S. Park D. J. Economou Parametric study of radio-frequency glow discharge using continuum model J. Appl. Phys. 68 9 4088 4890 Nov. 1990
4. P. Mozumder L. Loewenstein Method for semiconductor process optimization using functional representations of spatial variations and selectivity IEEE Trans. Comp., Hyb., Manufac. Tech. 15 3 311 316 June 1992 33 3932 148496
5. G. May J. Huang C. Spanos Statistical experimental design in plasma etch modeling IEEE Trans. Semi. Manufac. 4 2 83 98 May 1991 66 2611 79720
6. C. Himmel G. May Advantages of plasma etch modeling using neural networks over statistical techniques IEEE Trans. Semi. Manufac. 6 2 103 111 May 1993 66 5682 216928
7. M. Mocella J. Bondur T. Turner Etch process characterization using neural network methodology: A case study SPIE Proc. Module Metrology, Control and Clustering 1594 232 242 1991
8. E. Rietman E. Lory Use of neural networks in semiconductor manufacturing processes: An example for plasma etch modeling IEEE Trans. Semi. Manufac. 6 4 343 347 Nov. 1993 66 6689 267644
9. Y. Huang T. Edgar D. Himmelblau I. Trachtenberg Constructing a reliable neural network model for a plasma etching process using limited experimental data IEEE Trans. Semi. Manufac. 7 3 333 344 Aug. 1994 66 7544 311337
10. G. Box N. Draper Empirical Model Building and Response Surfaces 1987 Wiley New York
11. B. Kim G. May A new learning rule for neural process modeling Proc. 1993 World Congress on Neural Networks I 233 236 July 1993
12. S. Kirkpatrick C. Gelatt M. Vecchi Optimization by simulated annealing Science 220 671 680 May 1983
13. J. Freeman D. Skapura Neural Networks 1991 Addison-Wesley New York
14. L. Nadel L. Cooper P. Culicover M. Harnish Neural Connections, Mental Computation 1989 MIT Press Cambridge
15. D. Montgomery Introduction to Statistical Quality Control 1991 Wiley New York
16. J. Dulak B. Howard C. Steinbruchel Etch mechanism in the reactive ion etching of silicon nitride J. Vac. Sci. Tech. A 9 3 May/June 1991
17. C. Mogab A. Adams D. Flamm Plasma etching of Si and $\hbox{SiO}_{2}$ ̵The effect of oxygen additions to $\hbox{CF}_{4}$ plasmas J. Appl. Phys. 49 7 July 1978
18. P. Riley D. Hanson Comparison of etch rates of silicon nitride, silicon dioxide, and polycrystalline silicon upon $\\hbox{O}_{2}$ dilution of $\\hbox{CF}_{4}$ plasma J. Vac. Sci. Tech. B 7 6 Nov/Dec 1989
19. C. Steinbruchel H. Lehmann K. Frick Mechanism of dry etching of silicon dioxide J. Electrochem. Soc. 132 1 Jan. 1985