结合实际刻蚀数据的离子刻蚀产额优化建模方法

高扬福; 孙晓民; 宋亦旭; 阮聪

doi:10.7498/aps.63.248201

摘要

刻蚀表面仿真是研究等离子体刻蚀工艺过程机理的重要手段. 在刻蚀表面仿真方法中, 刻蚀表面演化模型和离子刻蚀产额模型直接决定了刻蚀表面演化结果. 但现有的刻蚀表面演化模型不够精确, 且目前离子刻蚀产额模型主要来自分子动力学仿真和物理实验, 而实际加工过程十分复杂,等效的离子刻蚀产额包含很多因素. 针对这些问题,本文首先对当前的刻蚀表面演化模型进行改进, 同时重新定义了离子刻蚀产额模型的优化目标, 并利用实际刻蚀加工数据来优化离子刻蚀产额模型. 为缩短优化模型所用时间, 本文采用并行方法来加速优化过程. 最后, 将得到的离子刻蚀产额模型参数应用于采用元胞自动机法的刻蚀工艺实际仿真过程中. 实验结果表明, 该优化建模方法确实提高了仿真的精确度, 同时优化过程所用时间也大大减少.

关键词:

Abstract

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.

Keywords:

作者及机构信息

1.
清华大学计算机科学与技术系, 智能技术与系统国家重点实验室, 清华大学信息科学与技术国家实验室, 北京 100084

基金项目: 国家重大科技专项(批准号: 2011ZX2403-002)资助的课题.

Authors and contacts

1.
Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory on Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Funds: Project supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2011ZX2403-002).

参考文献

[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

施引文献

[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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