Neural Network Modeling of Anion Exchange Using Reflection High-Energy Electron Diffraction Data
Arsenide/antimonide heterostructures have important applications in infrared detector, lasers, and high-=speed electronic devices. The performance, and hence, the manufacture of these devices is compromised by the difficulty of controlling the interface properties as a result of the tendency of group V elements to exchange. In this paper, a neural-network based model is developed to enable the control of the As-for-Sb exchange.
Keywords: Sb Compounds, Molecular Beam Expitaxy (MBE), Reflection High-Energy Electron Diffraction (RHEED), Neural Networks