Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping

Abstract

We have developed a phase mapping method based on machine learning analysis of reflection high-energy electron diffraction (RHEED) images. RHEED produces diffraction patterns containing a wealth of static and dynamic information and is commonly used to determine the growth rate, the growth mode, and the surface morphology of epitaxial thin films. However, the ability to extract quantitative structural information from the RHEED patterns that appear during film growth is limited by the lack of versatile and automated analysis techniques. We have created a deep learning-based analysis method for automating the identification of different RHEED pattern types that occur during the growth of a material. Our approach combines several supervised and unsupervised machine learning techniques and permits the extraction of quantitative phase composition information. We applied this method to the mapping of the structural phase diagram of ${\mathrm{Fe}}_{\mathrm{x}}{\mathrm{O}}_{\mathrm{y}}$ thin films grown by pulsed laser deposition as a function of growth temperature and oxygen pressure close to the hematite-magnetite phase boundary. The in situ RHEED-based mapping method produces results that are qualitatively similar to postsynthesis x-ray diffraction analysis.