Investigation and predictive modeling of the optical behavior of chalcogenide thin film using different artificial neural network technique

Abstract

The Te72Ge24As4 samples were recently created in our laboratory in bulk form using the traditional melt-quench method. For its optical characterization. The studied thin film samples have been created using physical vapor deposition. By selecting the 400 nm to 2500 nm spectral range of wavelength, the spectral of the experimental transmission T(λ) and reflectance R(λ) for the studied film samples have been employed to examine optical characteristics. First, we have determined the extinction coefficient ( k ) and refraction index ( n ) indices and their spectral distribution of them. Using Tauc’s theory, we then computed the optical band gap Eopt . Urbach energy Er is determined from the linear dependence of photon energy on the absorption coefficient which was taken as an indicator to identify the disorder degree in the films. The additional variables, like the dissipation and quality factors, the dielectric constant in complex form, optical, thermal, and elec trical conductivity, and volume/surface energy were measured. A comprehensive analysis and predictive modeling using various artificial neural networks (ANNs) techniques were applied to examine the optical behavior of the film samples studied. Materials made of chalcogenide are well-known for having special opti cal properties, making them appropriate for applications in photonics and opto electronics. We employed multiple architectures, including Feedforward Neural Networks (FNN) and Recurrent Neural Networks (RNN), to model the extinc tion coefficient ( k ) and the refractive index ( n ) of these films using experimental data