Prediction of Nanostructured ZnO Thin Film Properties Based on Neural Network

Abstract:

An approach in the prediction of zinc oxide (ZnO) thin films properties based on neural network is presented in this paper. The research had been focused on the electrical properties of ZnO. The sputtering power, substrate temperature, deposition time and oxygen ratio were selected as the input variables while the resistivity and conductivity were selected as the output. The numerical results obtained through the neural network model were compared with the experimental results. The result obtained from the system model of the proposed procedure was reasonably good and promising. Therefore, the prediction based on neural network model is a reliable approach compared to the traditional method of trial-and-error process.

• Periodical: Advanced Materials Research (Volume 832)
• Pages: 266-269
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.832.266
• Online since: November 2013
• Authors: Norlina M. Sabri, Nor Diyana Md Sin, Mazidah Puteh, Mohamad Rusop Mahmood
• Keywords: Electrical Properties, Neural Network (NN), Prediction, ZnO Thin Film

References

[1] T. G. D.M. Bagnall, Y.F. Chen, Z. Zhu, T. Yao, S. Koyama, M.Y. Shen, Optically pumped lasing of ZnO at room temperature, Applied Physics. 70 (1997) 2230-2232.

DOI: 10.1063/1.118824

[2] C. W. N. Y. Dai, Y. Zhang, Q.K. Li, Synthesis and optical properties of tetrapodlike zinc oxide nanorods, Chemical Physics Letters. 358 (2002) 83-86.

DOI: 10.1016/s0009-2614(02)00582-1

[3] T. S. S.J. Pearton, D.P. Norton, K. Ip, Y.W. Heo, Recent progress in processing and properties of ZnO, Prog. Mater Sci. 50 (2005) 293-340.

DOI: 10.1016/j.pmatsci.2020.100669

[4] T. S. D.P. Norton, Y.W. Heo, M.P. Ivill, K. Ip, S.J. Pearton, M.F. Chisholm, ZnO: growth, doping & processing, Mater. Today. 7 (2004) 34-40.

DOI: 10.1016/s1369-7021(04)00287-1

[5] A. I. S. Choopun, R.D. Vispute, W. Noch, A. Balsamo, R.P. Sharma, T. Venkatesan, D.C. Look, Oxygen pressure-tuned epitaxy and optoelectronic properties of laser-deposited ZnO films on sapphire, Appl. Phys. 75 (1999) 3947-3949.

DOI: 10.1063/1.125503

[6] C. N. X. W.S. Shi, O. Agyeman, Enhancement of the light emissions from zinc oxide films by controlling the post-treatment ambient, J. Appl. Phys. 91 (2002) 5640-5644.

DOI: 10.1063/1.1466527

[7] T. O. M. Izaki, Transparent zinc oxide films prepared by electrochemical reaction, Appl. Phys. 68 (1996) 2439-2440.

DOI: 10.1063/1.116160

[8] M. K. A. Tsukazaki, A. Ohtomo, T. Onuma, M. Ohtani, T. Makino, M. Sumiya, K. Ohtani, S.F. Chichibu, S. Fuke, Y. Segawa, H. Ohno, H. Koinuma, Repeated temperature modulation epitaxy for p-type doping and light-emitting diode based on ZnO, Nat. Mater. 4 (2005) 42-46.

DOI: 10.1038/nmat1284

[9] S. C. Lam, Y.C., Zhai, L.Y., Tai, K. & Fok, An evolutionary approach for cooling system optimization in plastic injection moulding, International Journal of Production Research. 42 (2204) 2047-2061.

DOI: 10.1080/00207540310001622412

[10] 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

[11] C. J. May, G. S., & Spanos, Fundamentals of semiconductor manufacturing and process control, Wiley & Sons, New Jersey , 2006.

[12] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall, New Jersey, 1999.

[13] I. K. F.M. Ham, Principles of Neurocomputing for Science and Engineering, Mc-Graw Hill, New York, 2001.

[14] A. Cevik, N. Atmaca, T. Ekmekyapar, and I. H. Guzelbey, Flexural buckling load prediction of aluminium alloy columns using soft computing techniques, Expert Systems with Applications. 36 (2009) 6332-6342.

DOI: 10.1016/j.eswa.2008.08.011

[15] S. V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic, IEEE Press, New York, 1996.

[16] 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

[17] G. S. Han, S.-S., & May, Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms, IEEE Transactions on Semiconductor Manufacturing. 10 (1997) 279-287.

DOI: 10.1109/66.572083

[18] M. Venkateswaran, S., Rai, M. M., Govindan, T. R., & Meyyappan, Neural network modeling of growth processes, Journal of the Electrochemical Society. 149 (2002) G137-G142.

DOI: 10.1149/1.1430721