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Prediction and tuning of the optical energy gap and refractive index of amorphous titania-alumina thin films prepared by atomic layer deposition using adaptive neuro-fuzzy inference system model

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Abstract

Titania was mixed with different percentages of alumina during the preparation of an 8 nm film by atomic layer deposition. The effect of mixing on the transmittance and absorption spectra was investigated. Increasing the alumina percentage increased the transmittance and decreased the absorption of light. The films showed an indirect allowed transition with an energy gap that slightly increased with increasing alumina percentage. The refractive index was calculated using four different methods. The average value of the refractive index decreases from 2.350 for pure titania to 2.314 for titania (70%) and alumina (30%). The optical properties were estimated by using ANFIS model. To achieve the best fit, different configurations were trained using MATLAB-R2021a, and the ANFIS optimal network of training the transmittance of titania-alumina thin films was determined. Experimental measurements were compared with the ANFIS simulated outputs and it is clear that the experimental data and the ANFIS simulated results almost coincide. The major goal is to employ the ANFIS model to predict the optical properties of the underlying titania thin film at alumina concentrations, which are experimentally unmeasured. Thus, the net is reduced in the number of samples, which is important in saving effort, time, and costs, which is an urgent requirement today. The method based on machine learning presented here also works when few measurement data are available and the relationship between the parameters is nonlinear, making the use of other numerical methods, e.g., inter/extrapolation, questionable. Compatibility between practical and theoretical results provides availability for more applications in the field of material science.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: The data that support the findings of this study are available from the corresponding author upon reasonable request [R.A. Mohamed; Rashaali@edu.asu.edu.eg].]

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Acknowledgements

Project no. TKP2021-NKTA-34 has been implemented with support provided by the National Research, Development, and Innovation Fund of Hungary, financed under the TKP2021-NKTA funding scheme.

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Authors and Affiliations

  1. Physics Department, Faculty of Education, Ain Shams University, Roxy, Cairo, Egypt

    S. S. Shenouda & R. A. Mohamed

  2. Department of Basic Sciences, Obour High Institute for Engineering and Technology, Cairo, Egypt

    S. S. Shenouda

  3. Department of Solid State Physics, Faculty of Science and Technology, University of Debrecen, PO Box 400, Debrecen, 4002, Hungary

    E. Baradács & Z. Erdélyi

  4. Department of Environmental Physics, Faculty of Science and Technology, University of Debrecen, Debrecen, Hungary

    E. Baradács

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Shenouda, S.S., Mohamed, R.A., Baradács, E. et al. Prediction and tuning of the optical energy gap and refractive index of amorphous titania-alumina thin films prepared by atomic layer deposition using adaptive neuro-fuzzy inference system model. Eur. Phys. J. Plus 138, 1024 (2023). https://doi.org/10.1140/epjp/s13360-023-04646-2

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