Advanced Materials Research ISSN: 1662-8985, Vol. 832, pp 260-265 doi:10.4028/www.scientific.net/AMR.832.260 © 2014 Trans Tech Publications, Switzerland
Online: 2013-11-21
Utilization of Soft Computing Techniques in Sputtering Processes: A Review N.M. Sabri1,a, M. Puteh1,b, M. Rusop2,c 1
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (Terengganu), 23000 Dungun, Terengganu
2
NANO-ElecTronic Centre, Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor
a
[email protected],
[email protected],
[email protected]
Keywords: soft computing; sputtering process; optimization; prediction
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. Introduction Soft computing techniques currently have been applied in various areas in engineering such as in civil, automotive, mechanical, electrical and also electronics. It has been explored into broad range of applications such as data analysis, data mining, computer graphics, intelligent control systems, pattern recognition, classifiers and modeling optimization [1]. In sputtering processes, soft computing techniques have been applied in the optimization of process parameters and in the prediction of thin film properties. Sputtering process is a non linear process, therefore it is appropriate to apply soft computing techniques to the process. Besides being an alternative to the current trial and error basis method, the techniques could save time, cost, and improve efficiency in the experiment designs. The trial-and-error process is not suitable for complex manufacturing processes as it is costly and time consuming [2]. The objective of the paper is to summarize the work related to the application of soft computing in sputtering process. Future possibilities of soft computing in sputtering process are also discussed in the paper. The paper is organized as follows. An overview of soft computing is provided in the first section while the next section provides the description about the reviews. The description of optimization and prediction based on soft computing techniques are provided in later section. Finally, the last section presents the future possibilities and concludes the paper.
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Overview of Soft Computing Soft computing is a group of methodologies which provide flexible information processing capabilities to solve real-life problems [3]. Among the major components of soft computing are Fuzzy Logic (FL), Neural Networks (NN) and Evolutionary Algorithms (EA) such as genetic algorithms, differential evolution and simulated annealing [4]. Founder of soft computing was Zadeh, who proposed a new generation computationally intelligent hybrid systems comprising of neural networks, fuzzy inference system, approximate reasoning and derivative free optimization techniques [5]. Soft computing is used to achieve tractability, robustness, and provide a low cost solution with a tolerance of imprecision, uncertainty, partial truth, and approximation [6]. This makes soft computing capable of solving problems in a cost-effective, analytical or complete manner, which is better than the conventional methods. Reviews Sources and Search Methods. The papers from this review were obtained from specific databases which were IEEE Xplore Digital Library, ScienceDirect Freedom Collection, Scientific.Net and Springer Online Journal Collection. A few of the papers were collected from conference proceedings, while most of them were collected from journals such as Expert Systems with Applications, Vacuum, Journal Of Materials Science: Materials In Electronics, International Journal Of Precision Engineering And Manufacturing, IEEE Transactions On Automation Science And Engineering, Applied Mechanics and Materials, Surface & Coatings Technology, Defect and Diffusion Forum and Journal Of Semiconductor Technology And Science. The related paper search was divided into two phases. The first phase was to search for the papers on the utilizing of soft computing in sputtering processes. The keywords used were sputtering parameter optimization and sputtering parameter prediction. Out of hundreds of papers, only 11 papers were related to soft computing utilization in sputtering processes. The papers gathered were published from 2008 to 2012. The next phase of searching was to find the literature review on the soft computing techniques with the keyword soft computing. Scope. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Other works on prediction and optimization of parameters but using different processes such as chemical vapor deposition, plasma etching, plasma spray process and watts bath were excluded in this study. This study includes different types of sputtering such as magnetron sputtering, ion-beam sputtering, ion-assisted sputtering, reactive sputtering and gas flow sputtering. Fig. 1 shows the number of papers related to application of soft computing in sputtering processes. It could be seen that the number of related research is increasing in 2012.
Fig. 1, Number of papers related to soft computing in sputtering processes.
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Optimization and Prediction Based on Soft Computing Techniques Optimization of Parameters. ANN and GA were the techniques that had been applied in most of the parameter optimizations. In a study to optimize parameter settings for the solar energy selective absorption film, ANN and GA had been integrated with traditional methods, which were Taguchi methods and desirability function [8]. ANN and GA were also used in the modeling and optimization of ITO/Al/ITO multilayer films characteristics, with thin film thickness and the annealing temperature as the studied parameters [9]. ANN and GA had been applied in various other processes such as in the optimization of transmittance characteristic for indium tin oxide [10], optimization of process parameters for semiconductor compounds [11], optimization of TiO thin film process parameters [7], in the process estimation and optimized recipes of ZnO:Ga thin film characteristics [12] and in process parameters optimization for a titanium dioxide (TiO2) thin film [13]. Table 1 shows the brief summary on the optimization of process parameters in sputtering process based on soft computing techniques. Table 1. A Brief Summary On Optimization Of Parameters Based On Soft Computing Technique No Author 1 [8]
Year 2012
Technique Taguchi methods, desirability function, ANN and GA. ANN and GA
Parameters Chamber pressure, sputtering power, nitrogen flow rate and process line speed.
2
[9]
2012
3
[10]
2010
ANN and GA.
4
[11]
2010
ANN
5
[7]
2010
Taguchi method, ANN and GA
Target current, oxygen flow rate, voltage and deposition time.
6
[12]
2010
ANN, GA
Thin-film thickness and annealing temperature. Outputs are sheet resistance, transmittance and figure of merit.
Parameters are thin film thickness and annealing temperature. The outputs are sheet resistance, optical transmittance, and the figure of merit. Wafer temperature, DC power, chamber pressure, Cesium (Cs) canister temperature, and Cs carrier flow rate. Oxygen and nitrogen.
Result The performance of the integrated procedure is better than that of Taguchi methods and traditional approach. Although the number of examples is small, the obtained result is satisfactory. NNet modeling results were well matched with the measured data.
Many interesting and helpful features were predicted from the model. All the findings could be effectively used for the optimization of ITO transmittance. The results indicate that the proposed neural network system exhibits superior performance to optimize the deposition parameters of TiCxOyNz. The result obtained from the system model of the proposed procedure is promising. It can be concluded that the proposed procedure is a very good approach in solving the problem of the process parameters design. The NNet models presented the good prediction on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films. GA which is applied to the NNet model is an effective method to predict the desired process condition for the characteristics of the ZnO:Ga thin films.
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[13]
2008
Taguchi method, ANN and GA
Parameters are voltage, time on the substrate during glowdischarge cleaning and rising temperature period; the target current, the oxygen flow rate and the time and voltage on the substrate during thin-film deposition period; and the bias value (-1). The output is the water contact angle.
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The result is quite satisfactory. The proposed modeling and optimization can be used to solve the optimal process parameters design problem.
Prediction of Thin Films Properties. The prediction of thin film properties had much involved fuzzy rules and ANN. Fuzzy Rule-Based Model had been applied to predict TiAlN coatings roughness, with sputtering power, substrate bias voltage and substrate temperature were set as the studied inputs [14]. Fuzzy rule based system had also been used in the prediction of TiN coating adhesion strength on Aerospace AL7075-T6 alloy [15]. Another study had proposed a progressive Taguchi-neural network model, by combining Taguchi method and artificial neural network to create a multi-objective prediction model for the prediction of resistivity and transmittance in sputtered GZO semiconducting transparent thin films [16]. ANN had also been used to predict the target voltage in reactive magnetron sputtering processes, while the target power level, reactive gas flow rate and its direction were used as inputs [17]. Table 2 shows the brief summary on the prediction of thin film properties based on soft computing techniques. Table 2, A Brief Summary On Prediction of Thin Film Properties Based On Soft Computing Technique No 1
Author [14]
Year 2012
Technique Fuzzy Logic
2
[15]
2012
Fuzzy Logic.
3
[16]
2011
Taguchi method and ANN.
4
[17]
2009
ANN
Parameters and Prediction Parameters are sputtering power, substrate bias voltage and substrate temperature. Predict the roughness of Titanium Aluminum Nitride (TiAlN) coatings. Parameters are DC power, DC bias voltage, and nitrogen flow rate. Predict the surface adhesion of the coated thin film of TiN on AL7075-T6 alloy. Paramaters are R. F. power, process pressure, target to substrate distance and deposition time. Predict the resistivity and transmittance in semiconducting transparent thin films.
Result Fuzzy rule-based model has much better predicting capability compared to the response surface regression model (RSM).
It is indicated that the fuzzy logic prediction model could be used to predict the surface adhesion of the coated thin film of TiN on AL7075-T6 alloy in a very accurate manner. Comparing the values of prediction verification and prediction values from each stage of the network verified the feasibility of the experimental prediction model combining Taguchi methods and ANN. Parameters are power level, Broyden–Fletcher–Goldfarb reactive gas flow rate and its Shanno (BFGS) algorithm gives direction. Predict the target the best result among other voltage in reactive learning algorithms used in the magnetron sputtering analysis. Both the training and processes. the test results are in very good agreement with the experimental results obtained in this work.
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Conclusion This paper provides 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 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. Despite improving the quality and minimizing the defect rates, the techniques could reduce unnecessary cost resulted from the use of the trial and error method for optimizing parameters. The soft computing techniques could also be applied to other engineering processes for the optimization and prediction of process parameters. 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. Not many researches have been done so far to utilize soft computing techniques in sputtering processes. Among the significant issues from the researches is the insufficient set of experimental data for algorithm training. 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 such as Simulated Annealing, Particle Swarm Optimization, Artificial Immune System and Gravitational Search Algorithm. The techniques could be used to deal with similar problems and their performances could be compared in order to find the best optimization techniques based on the specific processes.
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Nanoscience, Nanotechnology and Nanoengineering 10.4028/www.scientific.net/AMR.832
Utilization of Soft Computing Techniques in Sputtering Processes: A Review 10.4028/www.scientific.net/AMR.832.260 DOI References [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). 10.1080/00207540310001622412 [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. 10.1016/j.asoc.2009.09.004 [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. 10.1016/j.jnca.2005.06.001 [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. 10.1016/j.compag.2010.01.001 [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. 10.1016/j.eswa.2012.02.019 [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. 10.1007/s12540-010-1016-5 [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. 10.1016/j.eswa.2010.08.074 [14] A.S.M. Jaya, M. R. Muhamad, M.N. Abd Rahman and S.Z.M. Hashim, Application of Fuzzy RuleBased Model to Predict TiAlN Coatings Roughness, Applied Mechanics and Materials. 110-116 (2011) 10721079. 10.4028/www.scientific.net/AMM.110-116.1072 [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. 10.1007/s12541-012-0191-3 [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. 10.1016/j.surfcoat.2009.08.048
