Theoretical diagnostic and prediction of physical properties of quaternary InGaAsP compound using artificial neural networks optimized by the Levenberg Maquardt algorithm

Abstract

The quaternaries In 1−x Ga x As y P 1−y are the main promising elements for the fabrication of optoelectronic devices. The adjustment of their physical parameters is assumed by the change of the molar fraction x and y. These parameters can be affected by the variation of temperature and pressure. To make the theoretical diagnosis of these materials, it is fundamental to know the energy gap ' E g ' and the lattice parameter ' a ', over a wide range of chemical compositions 0 ≤ x ≤ 0.47 and 0 ≤ y ≤ 1 , at different temperatures and pressures. We show that by using the Artificial Neural Network method optimized by the Levenberg Maquardt algorithm ANN-LM, it is possible to obtain results very close to the experiment. The scatter plot and error calculation show that the ANN-LM model provides more accurate values of the lattice parameter than those calculated by Vegard's law. On the other hand, the energy gap values Eg(x, y, T) estimated, using the ANN-LM model, proved to be close to the experimental values that those calculated by the empirical equations. In addition, the ANN-LM method allowed us to estimate with great accuracy the values of the energy gap at different temperatures and pressures Eg(P, T). Our work provides crucial information on the physical properties of the quaternary without the use of approximations, and without taking into account the hypothesis of a perfect agreement between InGaAsP and InP substrate.

Bandgap energy modeling of the deformed ternary GaAs1-uNu by artificial neural networks

“…The artificial neural network method (ANN) has been used to solve several problems in different fields; it is a logic programming technique developed that is based on learning from experimental or analytical/theoretical data available to solve complex problems [ 23 , 24 , 25 , 26 ], which maps the input parameters to a specific output, and the formal neuron or perceptron mimics the functioning of a biological neuron to perform functions such as learning, memorization and decision, which is the basis of an ANN method. In this study, we calculated the bandgap energy of GaAN using an ANN and we conducted a comparative study between the experimental results, the results obtained by the ANN method, and the conduction band anticrossing model (CBAC) to test the robustness of the ANN method.…”