Exploring conduction mechanisms in chalcogenide thin films: an experimental and soft computing approach with ANN and GP techniques
D. M. Habashy, H.E. Atyia, H. I. Lebda
Abstract
Abstract The article consists of two main sections. In the first part, an experimental study was conducted to investigate the AC conduction of Se 83 Bi 17 (SB), Se 83 Bi 17 Ge 5 (SBG), and Se 83 Bi 17 Te 5 (SBT) in thin film forms. Thermal evaporation was utilized to produce films with different thicknesses. The measurements were performed at temperatures ranging from 293 to 393K, film thicknesses ranging from 230.2 to 543.9 nm, and frequencies ranging from 0.1 to 1000 kHz. The AC data were analyzed using the frequency power law. The values of the frequency exponent and their temperature dependence confirmed that the conduction process in the thin films is governed by the hopping mechanism. Additionally, the impact of introducing Te and Ge on the studied properties was also investigated in this study. The second section of the article focuses on estimating the AC conduction of Se 83 Bi 17 (SB), Se 83 Bi 17 Ge 5 (SBG), and Se 83 Bi 17 Te 5 (SBT) films using Artificial Neural Networks (ANNs) and Genetic Programming (GP). The experimental data was employed as inputs for training the ANNs and the GP models. The simulation and prediction results obtained from both models were compared with the experimental data and reported. Equations were developed to describe the behavior of the experimental data. The study concluded that the ANN approach is more suitable for simulating and forecasting AC conductivity.