PyCon APAC 2022｜一般演講 Talks｜國泰金控 Cathay Financial Holdings / 美光科技 Micron 冠名贊助 ✏️ 共筆 Note：https://hackmd.io/@pycontw/r1O1wpmyo ?? Slido：https://app.sli.do/event/mkwQmSfgPv8JCsh8Jny9xm ? 語言 Language：英文 English ? 層級 Level：中階 Intermediate ? 分類 Category：科學 Science ? 摘要 Abstract ? 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 Description ? Scanning Tunneling Microscope (STM) is a kind of microscope used to resolve surface structures under sub-nano scale such as features of sizes, shapes and orientations and their electronic band structures as well. It also allows for precise atomic manipulation based on which creation of quantum bits is achieved and propels building of a quantum computer. STM tip scanning is regularly affected by thermal drift, atoms getting picked up by, and moving around on the tip. I’ll use both straightforward NumPy and where possible introduce STM analysis libraries to show how these effects can be mitigated. Once that is done, I’ll first illustrate how edge detection and alignment with respect to surface atoms can be implemented directly in NumPy in various ways, then introduce OpenCV. Since the shapes can merge partially as they grow, I’ll describe how overlapping shapes can still be distinguished and measured. While OpenCV is powerful, it is necessary to do a significant amount of image pre-processing before reliable and repeatable shape identification is working smoothly. Finally, I'll move to Low Energy Electron Diffraction (LEED), which is also quite commonly used for determination of surface structure. LEED images can basically be viewed as 2D Fourier-transformed images of their real-space counterparts (STM images here). After showing how to use numpy's map coordinate to get real space coordinates, I'll show how to fit Gaussian spots and arcs, find them on the screen and once again get distributions from them. ? 講者介紹 About Speaker - Hsu-Kai Cheng ? I am currently a research assistant at Center for Condensed Matter Sciences in National Taiwan University (NTU). I received my bachelor's degree in the Department of Physics, NTU and my master’s degree in the Graduate Institute of Applied Physics, NTU. My scientific interests include surface science device development and characterization and my current research focuses on post-graphene materials such as black phosphorus and transition metal dichalcogenides. Especially, I aim to explore the properties of topological materials in which the interplay between topology and functions is so overwhelming and renders materials of this kind really promising for future application. #pycontw #pyconapac2022 #python #scanning #tunneling #microscopy #imageprocessing #featurerecognition #featureextraction Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blogger: https://pycontw.blogspot.com ... https://www.youtube.com/watch?v=8g6_Q2zMJ0g