Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Abstract

Abstract The current research investigates the conductivity σ AC of glassy As 4 Ge 24 Te 72 thin films through a combined experimental and theoretical approach. It sheds light on the complex relationship between σ AC , frequency, temperature, and film thickness. Analysis reveals that σ AC follows ω r law and exhibits hopping behavior. To effectively model and predict σ AC , an adaptive neuro-fuzzy inference system (ANFIS) is utilized. The ANFIS model successfully simulates the experimental data, showing high accuracy. Additionally, the prediction of experimentally measured values of σ AC is processed as a testing step and provides acceptable results. That enables ANFIS to enlarge the scale and complete the missing parts in the trained datasets by predictions for unmeasured σ AC values. The ANFIS network was built using MATLAB-R2017a. It consists of two inputs (frequency and temperature) and one output (AC conductivity). The precision is confirmed by calculating the modeling errors across the training process. The mean absolute errors MAE through the modeling process are not exceeding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> <mml:mo>.</mml:mo> </mml:math> This average error value demonstrates the model’s effective ability. The alignment between experimental and ANFIS modeling results open up exciting possibilities for future research. By predicting the properties of the understudy material using ANFIS model, we can pave the way for more compact and efficient electrical devices.