Photo by testalizeme from unsplash
Recent advances in scanning transmission electron microscopy (STEM) and scanning tunneling microscopy (STM) allow unprecedented opportunities in probing the materials structural parameters and electronic properties in real space with an… Click to show full abstract
Recent advances in scanning transmission electron microscopy (STEM) and scanning tunneling microscopy (STM) allow unprecedented opportunities in probing the materials structural parameters and electronic properties in real space with an angstrom-level precision. These experimental capabilities require development of tools for the rapid, physics-guided analysis of the very large amount of data generated by modern day microscopes, ideally in a real-time. Here we argue that one of the most promising methods for creating such an AI-powered microscope is based on deep neural networks [1, 2].
Journal Title: Microscopy and Microanalysis
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!