Bandgap energy modeling of the deformed ternary GaAs1-uNu by artificial neural networks
A. Tarbi,
T. Chtouki,
Y. Elkouari,
H. Erguig,
A. Migalska-Zalas,
A. Aissat
Affiliations
A. Tarbi
Laboratory of Condensed Matter and Renewable Energy, Faculty of Sciences and Technology, University Hassan II of Casablanca, BP146 Mohammedia, Morocco; Corresponding author.
T. Chtouki
Superior School of Technology, Materials Physics and Subatomic Laboratory, Ibn-Tofail University, PB 242, 14000 Kenitra, Morocco
Y. Elkouari
Laboratory of Condensed Matter and Renewable Energy, Faculty of Sciences and Technology, University Hassan II of Casablanca, BP146 Mohammedia, Morocco
H. Erguig
Superior School of Technology, Materials Physics and Subatomic Laboratory, Ibn-Tofail University, PB 242, 14000 Kenitra, Morocco
A. Migalska-Zalas
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Al. Armii Krajowej 13/15, 42201 Czestochowa, Poland
A. Aissat
Faculty of Technology, University of Saad Dahlab Blida. 1, Blida, Algeria; Corresponding author.
Appraising the bandgap energy of materials is a major issue in the field of band engineering. To better understand the behavior of GaAs1-uNu material, it is necessary to improve the applied calculation methodologies. The band anticrossing model (BAC) allows modeling of the bandgap energy when diluted nitrogen is incorporated into the material. The model can be improved using artificial neural networks (ANN) as an alternative solution, which is rarely applied. Our goal is to study the efficiency of the (ANN) method to gauge the bandgap energy of the material from experimental measurements, considering the extensive strain due to the lattice mismatch between the substrate and the material. This makes the GaAsN material controllable with (ANN) method, and is a potential candidate for the fabrication of ultrafast optical sensors.
