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Received June 16, 2009
Accepted June 29, 2009
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Optimal design of multi-nozzle etching process for shadow mask
School of Display and Chemical Engineering, Yeungnam University, 214-1, Dae-dong, Gyeongsan, Gyeongbuk, Korea 1Samsung Everland Inc., 87 Euljiro 1ga, Jung-gu, Seoul, Korea
Korean Journal of Chemical Engineering, November 2009, 26(6), 1519-1527(9), 10.1007/s11814-009-0318-0
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Abstract
This paper presents a new design approach of a multi-nozzle etching process which is the core system for the production of a shadow mask. The shadow mask, which is a thin metal plate with a huge number of small holes in regular patterns, is a key component of televisions and computer monitors. The shadow mask plays an important role in controlling the definition, color and distinction of televisions and computer monitors. Thus, the development of a rigorous and systematic design method for a multi-nozzle etching process to manufacture the shadow mask is beneficial particularly from the viewpoint of increasing efficiency and improving productivity. The proposed design method is based on simulating the complex spraying pattern using a Monte-Carlo method, whereas a stochastic method, socalled genetic algorithm, is used for an optimization tool. In such a highly complex solution space, the genetic algorithm searches optimal solutions efficiently and effectively. The simulation of spraying pattern for the multi-nozzle system and the genetic algorithm are coded by C language, while the graphic representations are attained by MATLAB graphic tools.
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References
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De Jong KA, Proceedings of the International Conference on Genetic Algorithms and Their Application, 210 (1995)
Maynard RB, Moscony JJ, Saunders MH, Ferric chloride etching of Invar, RCA Rev., 47, 88 (1986)
Kwon GJ, Sun HY, Sohn HJ, J. Electrochem. Soc., 142(9), 3016 (1995)
Hariki M, Nishi A, Morita M, Tetsu-to-hagane., 83, 257 (1997)
Mccreery GE, Stoots CM, Int. J. Multiph. Flow, 22(3), 431 (1996)
Sommerfield M, Qiu HH, Int. J. Heat Fluid Fl., 19, 10 (1998)
Panchagnula MV, Sojka PE, Fuel, 78(6), 729 (1999)
Holland JH, Adaptation in natural and artificial systems, University of Michigan Press, MI (1975)
Goldberg DE, Lingle R, Proceedings of the International Conference on Genetic Algorithms and Their Applications, 154 (1985)
Goldberg DE, Engineering with Computers, 3, 35 (1987)
Goldberg DE, Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Co. (1989)
Rawlins GJE, Foundation of genetic algorithms, Morgan Kanfmann Publishers, San Mateo (1991)
Davis L, Genetic algorithms and simulated annealing, Morgan Kanfmann Publishers, San Mateo (1987)
Jung JH, Lee CH, Lee IB, Comput. Chem. Eng., 22(11), 1725 (1998)
Davis L, Handbook of genetic algorithms, Van Nostrand ReinHold, New York ( (1991)
Davidor Y, Genetic algorithms and robotics: A heuristic strategy for optimization, World Scientific, Singapore (1991)
Park S, Cho H, Lee H, Jeon L, ‘92 KACC (Domestic) at Seoul, 863 (1992)
Kim Y, Kang H, Jeon H, ‘92 KACC (Domestic) at Seoul, 698 (1992)
De Jong KA, Proceedings of the International Conference on Genetic Algorithms and Their Application, 210 (1995)
