Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates
Abstract Advent in machine learning is leaving deep impact on various sectors including material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates. The results showed that polynomial regression machine learning algorithm outperforms all other machine learning models by yielding the coefficient of determination of 0.96 approximately and mean square error of 0.04 respectively.
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López-Rubio, A., Blanco-Padilla, A., Oksman, K. and Mendoza, S., 2020. Strategies to Improve the Properties of Amaranth Protein Isolate-Based Thin Films for Food Packaging Applications: Nano-Layering through Spin-Coating and Incorporation of Cellulose Nanocrystals. Nanomaterials, 10(12), p.2564.
Fu, J., Li, M., Liu, G., Ma, S., Zhu, X., Ma, C., Cheng, D. and Yan, Z., 2020. Robust ceramic based self-lubricating coating on Al–Si alloys prepared via PEO and spin-coating methods. Wear, 458, p.203405.
Kelso, M.V., Mahenderkar, N.K., Chen, Q., Tubbesing, J.Z. and Switzer, J.A., 2019. Spin coating epitaxial films. Science, 364(6436), pp.166-169.
Fakhri, M.A., Salim, E.T., Wahid, M.H.A., Abdulwahhab, A.W., Salim, Z.T. and Hashim, U., 2019. Heat treatment assisted-spin coating for LiNbO3 films preparation: their physical properties. Journal of Physics and Chemistry of Solids, 131, pp.180-188.
Lu, C. and Tang, L., 2019. Comment on “Spin coating epitaxial films”. Science, 365(6458), p.eaay3894.
Hadi, S.A., Humood, K.M., Abi Jaoude, M., Abunahla, H., Al Shehhi, H.F. and Mohammad, B., 2019. Bipolar Cu/HfO 2/p++ Si Memristors by Sol-Gel Spin Coating Method and Their Application to Environmental Sensing. Scientific reports, 9(1), pp.1-15.
Ossila (2019): Spin Coating: A Guide to Theory and Techniques. https://www.ossila.com/pages/spin-coating
Wakabayashi, Y.K., Otsuka, T., Krockenberger, Y., Sawada, H., Taniyasu, Y. and Yamamoto, H., 2019. Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films. APL Materials, 7(10), p.101114.
Ding, Y., Zhang, Y., Ren, Y.M., Orkoulas, G. and Christofides, P.D., 2019. Machine learning-based modeling and operation for ALD of SiO2 thin-films using data from a multiscale CFD simulation. Chemical Engineering Research and Design, 151, pp.131-145.
Greco, A., Starostin, V., Karapanagiotis, C., Hinderhofer, A., Gerlach, A., Pithan, L., Liehr, S., Schreiber, F. and Kowarik, S., 2019. Fast fitting of reflectivity data of growing thin films using neural networks. Journal of applied crystallography, 52(6).
Banko, L., Lysogorskiy, Y., Grochla, D., Naujoks, D., Drautz, R. and Ludwig, A., 2020. Predicting structure zone diagrams for thin film synthesis by generative machine learning. Communications Materials, 1(1), pp.1-10.
Panfilova, E.V. and Galaganova, E.N., 2020, April. Neural Network modeling of Thin Films Deposition Processes in the Master’s Degree Programs “Electronics and Nanoelectronics” and “Nanoengineerig”. In IOP Conference Series: Materials Science and Engineering (Vol. 781, No. 1, p. 012016). IOP Publishing.
Mishra, A., & Dixit, D. (2021). Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates . ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 267–279. https://doi.org/10.14201/ADCAIJ2021103267279
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