Data-Driven Azimuthal RHEED Construction for In Situ Crystal Growth Characterization

Abstract

Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe 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 full wafer rotation. However, ARHEED requires precise information about the rotation angle, as well as 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 used 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.