Hybrid neural network modeling of anion exchange at the interfaces of mixed anion III-V heterostructures grown by molecular beam epitaxy

A hybrid neural network model is constructed by characterizing the growth of GaAs_1-yP_y-GaAs superlattices (SLs) grown on [001] GaAs substrates by molecular beam epitaxy. These heterostructures are formed by the P₂ exposure of an As-stabilized GaAs surface, and ex situ high-resolution X-ray diffraction (HRXRD) is performed to determine the phosphorus composition at the interfaces. A first-order kinetic model is then developed to describe the mechanisms of anion exchange, surface desorption, and diffusion. A semi-empirical hybrid neural network is used to estimate the parameters of the kinetic model and analyze the microscopic processes occurring at the interfaces of the mixed anion III-V heterostructures. The phosphorus diffusion process in GaAs is estimated to have a diffusion coefficient of D=1.4×10^-14exp(-0.11 eV/k_BT_s) cm²·s^-1 for samples with P_As4=4×10^-6 torr and exhibits enhanced phosphorus intermixing for samples with lower As-stabilizing fluxes.