Bandgap energy modeling of the deformed ternary GaAs1-uNu by Artificial Neural Networks

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.

Automated summary

Researchers evaluate the bandgap energy of GaAsN material using the band anticrossing model and artificial neural networks method, considering the strain caused by lattice mismatch. This method makes GaAsN a potential material for the fabrication of ultrafast optical sensors.