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 GaAs1-yPy-GaAs superlattices (SLs) grown on (001) GaAs substrates by molecular beam epitaxy. These heterostructures are formed by the P-2 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 x 10(-14) exp(-0.11 eV/k(B)T(s)) cm(2) (.) s(-1) for samples with P-As4 = 4 x 10(-6) torr and exhibits enhanced phosphorus intermixing for samples with lower As-stabilizing fluxes.