Utilization of Soft Computing Techniques in Sputtering Processes: A Review

Article Preview

Abstract:

This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

260-265

Citation:

Online since:

November 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] R. Tiwari, A. Knowles, J. Avineri, E. Dahal, K. Roy, Applications of Soft Computing: Recent Trends, Springer, New York, 2006.

DOI: 10.1007/978-3-540-36266-1

Google Scholar

[2] Y.C. Lam, L.Y. Zhai, K. Tai, S.C. Fok, An evolutionary approach for cooling system optimization in plastic injection moulding, International Journal of Production Research. 42 (2004) 2047-2061.

DOI: 10.1080/00207540310001622412

Google Scholar

[3] M. Ko, A. Tiwari, and J. Mehnen, A review of soft computing applications in supply chain management, Applied Soft Computing. 10 (2010) 661-674.

DOI: 10.1016/j.asoc.2009.09.004

Google Scholar

[4] U. Maulik, Analysis of gene microarray data in a soft computing framework, Applied Soft Computing. 11 (2011) 4152-4160.

DOI: 10.1016/j.asoc.2011.03.004

Google Scholar

[5] A. Abraham, R. Jain, J. Thomas and S.Y. Han, D-SCIDS: Distributed soft computing intrusion detection system, Journal of Network and Computer Applications. 30 (2007) 81-98.

DOI: 10.1016/j.jnca.2005.06.001

Google Scholar

[6] Y. Huang, Y. Lan, S.J. Thomson, A. Fang, W.C. Hoffmann and R.E. Lacey, Development of soft computing and applications in agricultural and biological engineering, Computers and Electronics in Agriculture. 71 (2010) 107-127.

DOI: 10.1016/j.compag.2010.01.001

Google Scholar

[7] W. Ho, J. Tsai and G. Hsu, Process Parameters Optimization: A Design Study for TiO Thin Film of Vacuum Sputtering Process, IEEE Transactions on Automation Science And Engineering. 7 (2010) 143-146.

DOI: 10.1109/tase.2009.2023673

Google Scholar

[8] H.C. Lin, C.T. Su, C.C. Wang, B.H. Chang and R.C. Juang, Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms, Expert Systems with Applications. 39 (2012) 12918-12925.

DOI: 10.1016/j.eswa.2012.05.032

Google Scholar

[9] E. N. Cho, P. Moon, C.E. Kim and I. Yun, Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm, Expert Systems with Applications. 39 (2012) 8885-8889.

DOI: 10.1016/j.eswa.2012.02.019

Google Scholar

[10] B. Kim, S.J. Lee, C.H. Min and T.S. Kim, Optimization of transmittance characteristic of indium tin oxide film using neural networks, Metals and Materials International. 16 (2010) 793-797.

DOI: 10.1007/s12540-010-1016-5

Google Scholar

[11] C.W. Yeh and K.R. Wu, Neural network-based system for optimizing process parameters of semiconductor compounds, 2010 2nd IEEE International Conference on Information Management and Engineering. (2010) 214-218.

DOI: 10.1109/icime.2010.5477804

Google Scholar

[12] C.E. Kim, P. Moon, I. Yun, S. Kim, J.M. Myoung, H.W. Jang and J. Bang, Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications, Expert Systems with Applications. 38 (2011) 2823-2827.

DOI: 10.1016/j.eswa.2010.08.074

Google Scholar

[13] C. Science, N. Pingtung and M.S. Road, Optimal Process Design Using Soft Computing Approaches, SICE Annual Conference. (2008) 344-347.

Google Scholar

[14] A.S.M. Jaya, M. R. Muhamad, M.N. Abd Rahman and S.Z.M. Hashim, Application of Fuzzy Rule-Based Model to Predict TiAlN Coatings Roughness, Applied Mechanics and Materials. 110-116 (2011) 1072-1079.

DOI: 10.4028/www.scientific.net/amm.110-116.1072

Google Scholar

[15] E. Zalnezhad, A.A.D. M. Sarhan and M. Hamdi, Prediction of TiN coating adhesion strength on aerospace AL7075-T6 alloy using fuzzy rule based system, International Journal of Precision Engineering and Manufacturing. 13 (2012) 1453-1459.

DOI: 10.1007/s12541-012-0191-3

Google Scholar

[16] C.B. Yang, Multi-objective prediction model for the establishment of sputtered GZO semiconducting transparent thin films, Journal of Intelligent Manufacturing. (2011).

DOI: 10.1007/s10845-011-0614-5

Google Scholar

[17] K. Danisman, S. Danisman, S. Savas and I. Dalkiran, Modelling of the hysteresis effect of target voltage in reactive magnetron sputtering process by using neural networks, Surface and Coatings Technology. 204 (2009) 610-614.

DOI: 10.1016/j.surfcoat.2009.08.048

Google Scholar