Machine learning-based method for predicting C-V-T characteristics and electrical parameters of GaAs/AlGaAs Multi-Quantum Wells Schottky diodes

In this work, two models of artificial neural networks are developed to predict the electrical parameters and capacitance-voltage characteristics of GaAs/AlGaAs multi-quantum wells Schottky diodes at different temperatures. Capacitance-Voltage-Temperature (C-V-T) characteristics for voltages and temperatures in the ranges (-4V to 0V) and (20K to 400K), respectively, were used to assess the effectiveness of the proposed approach. The first model (Model 1) is used to evaluate how well the neural network predicts the C-V-T characteristics. The second simulation, known as Model 2, was constructed to simultaneously overcome the problems of determining the electrical parameters and predicting C-V-T characteristics. Model 2 allows the calculation of the built-in voltage, effective density, and capacitance. Three-fold cross-validation and mean square error are used to assess the effectiveness of the developed models. The results clearly demonstrate the high prediction accuracy of the electrical parameters and C-V characteristics at all temperatures. After training, Model 1 the Mean Square Error performance is at 1450 epochs, whereas Model 2 MSE is at 642 epochs. According to the error distribution frequency histogram, about 95% of errors for Model 1 and Model 2 lie between [0.00535 and 0.005608] and [0.00328 and 0.00333], respectively. The R-values that correspond to the training and validation datasets for both models are close to one (0.9999). Parameters determination results have been compared against those obtained using ant lion optimizer based method. It was found that the results obtained from the neural networks models strongly agree with the experimental data