Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization

Abstract

Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe the surface reconstruction during MBE growth. However, raw RHEED patterns are difficult to interpret, especially when the wafer is rotating. A more accessible representation of the information is therefore the so-called Azimuthal RHEED (ARHEED), an angularly resolved plot of the electron diffraction pattern during a full wafer rotation. However, ARHEED requires precise information about the rotation angle as well as of the position of the specular spot of the electron beam. We present a Deep Learning technique to automatically construct the Azimuthal RHEED from bare RHEED images, requiring no further measurement equipment. We use two artificial neural networks: an image segmentation model to track the center of the specular spot and a regression model to determine the orientation of the crystal with respect to the incident electron beam of the RHEED system. Our technique enables accurate, and potentially real-time ARHEED construction on any growth chamber equipped with a RHEED system.