A Combined Experimental and Theoretical Study of the Optical Behavior of Se-Ge-Ga-Sb Chalcogenide Thin Films. | ||||
| Egyptian Journal of Solids | ||||
| Article 3, Volume 46, Issue 1, 2024, Page 33-78 PDF (3.71 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ejs.2024.300535.1047 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Doaa Habashy  1; amira hasanen2; mahmoud elbakry3; rasha aly2; rokia aly4
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| 1department of physics, faculty of education, ai shams university | ||||
| 2faculty of education, ain shams university | ||||
| 3faculty of education ain shams university | ||||
| 4department of physics | ||||
| Abstract | ||||
| This study focused on the characterization of optical properties for chalcogenide glass (〖Se〗_48.62 〖Ge〗_28.09 〖Ga〗_6.34 〖Sb〗_11.30) prepared via quenching procedure and was thermally deposited as thin films on glass substrates using thermal evaporation process. The amorphous nature of the evaporated films was confirmed through X-ray diffraction analysis. The chemical composition of the prepared sample was obtained using energy dispersive X-spectroscopy (EDX). The optical properties of the thin films, including transmittance (T), reflection (R), thickness (t), and refractive index (n), were investigated using spectrophotometry (190-2500 nm). The Swanepoel technique was employed to calculate the thickness and n values from the transmission data. The Wemple-DiDomenico model was applied to estimate dispersion factors (E_ο and E_d) and dielectric constants from the refractive index data. The optical energy gap (E_g^opt) for the studied composition was determined. The analysis of optical absorption revealed both permitted direct and indirect transitions. To model the optical constants, an artificial neural network (ANN) was trained using experimental data. Various ANN configurations were tested, and the best one with minimal error was selected. The ANN model demonstrated a satisfactory match with the findings. A mathematical equation describing the optical behavior of the samples was derived. The selected ANN model's performance was admirable, as it could accurately predict optical parameters for unmeasured thicknesses. In conclusion, the ANN approach proved to be a valuable tool for modeling the optical properties of 〖Se〗_48.62 〖Ge〗_28.09 〖Ga〗_6.34 〖Sb〗_11.30 Thin films, exhibiting high accuracy and excellent predictive capability. | ||||
| Keywords | ||||
| Optical properties; Chalcogenide glasses; Modeling; Prediction; and Artificial Neural Network ANN model | ||||
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