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The profile surface simulation is an important method to study the ion etching mechanism. In profile surface simulation, the result of surface evolution is primarily determined by the surface evolution model and the etching yield optimization model as well. However, the currently available surface evolution model is not accurate enough. What's more, most of the data used in etching yield optimization model are based on simulation, while no factual data are used to optimize the parameters of ion etching yield model. In order to solve these problems, the accuracy of current evolution model is improved, the optimal objects of etching yield model are redefined, and the factual etching data are introduced to optimize the etching yield model for the first time. In this paper, parallel method is also adopted to speed up the optimization process, whose optimized parameters are then applied to the etching simulation process that is based on cellular automata. The experimental results show that our proposed approach does improve the accuracy of simulation and greatly shorten the optimization process.
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Keywords:
- factual etching data /
- etching yield model /
- etching velocity /
- multi-object evolution algorithm
[1] Kawai H 2008 Ph. D. Dissertation (Cambridge: Massachusetts Institute of Technology)
[2] Saussac J, Margot J, Chaker M 2009 J. Vac. Sci. Technol. A 27 130
[3] María E L, Luis A 2014 Chin. Phys. B 23 050701
[4] Song Y R, Jiang G P, Gong Y W 2013 Chin. Phys. B 22 040502
[5] Levinson J A, Shaqfeh E S G, Balooch M, Hamza A V 2000 J. Vac. Sci. Technol. B 18 172
[6] Tuda M, Nishikawa K, Ono K 1997 J. Appl. Phys. 81 960
[7] Osher S, Sethian J A 1988 J. Comput. Phys. 79 12
[8] Osher S, Fedkiw R P 2001 J. Comput. Phys. 169 463
[9] Chang J P, Arnold J C, Zau G C H, Shin H S, Sawin H H 1997 J. Vac. Sci. Technol. A 15 1853
[10] Gou F, Kleyn A W, Gleeson M A 2008 Int. Rev. Phys. Chem. 27 229
[11] Gao Y F, Song Y X, Sun X M 2014 Acta Phys. Sin. 63 048201 (in Chinese) [高扬福, 宋亦旭, 孙晓民 2014 63 048201]
[12] Liu H H, Liu Y H 2012 Chin. Phys. B 21 026102
[13] Liu J F 2009 Chin. Phys. B 18 2615
[14] Ishibuchi H, Sakane Y, Tsukamota N, Nojima Y 2009 Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, USA, October 11-14, 2009 p1758
[15] Zheng S L, Song Y X, Sun X M 2013 Acta Phys. Sin. 62 108201 (in Chinese) [郑树琳, 宋亦旭, 孙晓民 2013 62 108201]
[16] Zhang Q, Li H 2007 IEEE T. Evolut. Comput. 11 712
[17] Nebro A J, Durillo J J 2010 Learning and Intelligent Optimization (Venice: Springer Berlin Heidelberg) pp303-317
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[1] Kawai H 2008 Ph. D. Dissertation (Cambridge: Massachusetts Institute of Technology)
[2] Saussac J, Margot J, Chaker M 2009 J. Vac. Sci. Technol. A 27 130
[3] María E L, Luis A 2014 Chin. Phys. B 23 050701
[4] Song Y R, Jiang G P, Gong Y W 2013 Chin. Phys. B 22 040502
[5] Levinson J A, Shaqfeh E S G, Balooch M, Hamza A V 2000 J. Vac. Sci. Technol. B 18 172
[6] Tuda M, Nishikawa K, Ono K 1997 J. Appl. Phys. 81 960
[7] Osher S, Sethian J A 1988 J. Comput. Phys. 79 12
[8] Osher S, Fedkiw R P 2001 J. Comput. Phys. 169 463
[9] Chang J P, Arnold J C, Zau G C H, Shin H S, Sawin H H 1997 J. Vac. Sci. Technol. A 15 1853
[10] Gou F, Kleyn A W, Gleeson M A 2008 Int. Rev. Phys. Chem. 27 229
[11] Gao Y F, Song Y X, Sun X M 2014 Acta Phys. Sin. 63 048201 (in Chinese) [高扬福, 宋亦旭, 孙晓民 2014 63 048201]
[12] Liu H H, Liu Y H 2012 Chin. Phys. B 21 026102
[13] Liu J F 2009 Chin. Phys. B 18 2615
[14] Ishibuchi H, Sakane Y, Tsukamota N, Nojima Y 2009 Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, USA, October 11-14, 2009 p1758
[15] Zheng S L, Song Y X, Sun X M 2013 Acta Phys. Sin. 62 108201 (in Chinese) [郑树琳, 宋亦旭, 孙晓民 2013 62 108201]
[16] Zhang Q, Li H 2007 IEEE T. Evolut. Comput. 11 712
[17] Nebro A J, Durillo J J 2010 Learning and Intelligent Optimization (Venice: Springer Berlin Heidelberg) pp303-317
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