Python under a microscope: Scanning Tunneling Microscopy, image processing, feature recognition and extraction of statistics

摘要

Scanning Tunneling Microscope is widely used for 2D material characterization and even more, for building quantum computer at the cutting edge of science. A typical surface science experiment can generate hundreds of STM images each containing multiple structures to be identified. Since each investigation is different, it needs to draw on highly flexible methods algorithmically and integrate several image processing techniques. In this talk I’ll present an end-to-end Pythonic solution: reading raw STM data, implementing various STM artifact, noise filtering schemes and flattening. Then I’ll discuss how to search for, identify, count and measure different surface structures, particularly some epitaxial islands. Finally I’ll show statistical results of size and angular distributions, and apply some similar image processing, feature recognition and image interpolation algorithms to extract angular distributions from electron diffraction measurements and compare the results.

說明