Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks
In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO<sub>2</sub> film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the ge...
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2023-04-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/7/2865 |
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| author | Katarina Lj Djordjević Dragana K. Markushev Marica N. Popović Mioljub V. Nesić Slobodanka P. Galović Dragan V. Lukić Dragan D. Markushev |
| author_facet | Katarina Lj Djordjević Dragana K. Markushev Marica N. Popović Mioljub V. Nesić Slobodanka P. Galović Dragan V. Lukić Dragan D. Markushev |
| author_sort | Katarina Lj Djordjević |
| collection | DOAJ |
| description | In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO<sub>2</sub> film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy. |
| first_indexed | 2024-03-11T05:31:54Z |
| format | Article |
| id | doaj.art-81ce537e8c1c4a75a638f9b7dd95fa93 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T05:31:54Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-81ce537e8c1c4a75a638f9b7dd95fa932023-11-17T17:06:16ZengMDPI AGMaterials1996-19442023-04-01167286510.3390/ma16072865Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural NetworksKatarina Lj Djordjević0Dragana K. Markushev1Marica N. Popović2Mioljub V. Nesić3Slobodanka P. Galović4Dragan V. Lukić5Dragan D. Markushev6“Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, P.O. Box 522, 11000 Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, University of Belgrade, Zemun, 11080 Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, University of Belgrade, Zemun, 11080 Belgrade, Serbia“Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, P.O. Box 522, 11000 Belgrade, Serbia“Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, P.O. Box 522, 11000 Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, University of Belgrade, Zemun, 11080 Belgrade, SerbiaInstitute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, University of Belgrade, Zemun, 11080 Belgrade, SerbiaIn this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO<sub>2</sub> film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy.https://www.mdpi.com/1996-1944/16/7/2865thin-filmTiO<sub>2</sub>photoacousticartificial neural networksthermal diffusionthermal expansion |
| spellingShingle | Katarina Lj Djordjević Dragana K. Markushev Marica N. Popović Mioljub V. Nesić Slobodanka P. Galović Dragan V. Lukić Dragan D. Markushev Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks Materials thin-film TiO<sub>2</sub> photoacoustic artificial neural networks thermal diffusion thermal expansion |
| title | Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks |
| title_full | Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks |
| title_fullStr | Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks |
| title_full_unstemmed | Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks |
| title_short | Photoacoustic Characterization of TiO<sub>2</sub> Thin-Films Deposited on Silicon Substrate Using Neural Networks |
| title_sort | photoacoustic characterization of tio sub 2 sub thin films deposited on silicon substrate using neural networks |
| topic | thin-film TiO<sub>2</sub> photoacoustic artificial neural networks thermal diffusion thermal expansion |
| url | https://www.mdpi.com/1996-1944/16/7/2865 |
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