Neural networks modeling for refractive indices of semiconductors

Abstract

This paper uses an artificial neural network (ANN) and Levenberg–Marquardt training algorithm to model the nonlinear relationship between refractive index and energy gap in semiconductors. The predicted simulation values of the ANN are in accordance with the experimental data. An error deviation (Δn) was estimated for different models. The lowest deviation is given by the ANN model. The ANN model performance was also tested for some compounds not included in the training and was found to be in good agreement with the experimental data. High precision of the NN model is demonstrated as well as good generalization performance.