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Title: | REDUCED AND COMPRESSED DATA REPRESENTATIONS IN 4D SCANNING TRANSMISSION ELECTRON MICROSCOPY | Authors: | ZHANG CHENG | ORCID iD: | orcid.org/0009-0003-7289-8278 | Keywords: | 4D-STEM,unsupervised machine learning | Issue Date: | 22-Jan-2023 | Citation: | ZHANG CHENG (2023-01-22). REDUCED AND COMPRESSED DATA REPRESENTATIONS IN 4D SCANNING TRANSMISSION ELECTRON MICROSCOPY. ScholarBank@NUS Repository. | Abstract: | 4D Scanning Transmission Electron Microscopy (STEM) contains rich momentum-resolved scattering information, which contains annular bright field (ABF), low annular annul dark field (LAADF), and high annular annul dark field (HAADF) images. However, 4D-STEM data’s large file size makes it hard to store and analyze. We show that for quasi-periodic molybdenum disulfide (MoS2) samples imaged with a focused probe, their 4D-STEM data is highly redundant. Therefore, distinguishing and extracting domain features with an acceptable range of loss from 4D-STEM data is possible and necessary. Based on principal component analysis (PCA) and Zernike component methods, we show that the 4D-STEM data is highly redundant; hence it can be reduced and compressed with minimal impact on imaging. We also examine the physical interpretation of specific Zernike modes of the 4D STEM data: information about the probe’s beam while scanning the sample and the phase contrast of different elements in the model. In our MoS2 4D-STEM data, our Zernike projection suggested a possible “stacking” of sulphuric atoms at rare interstitial sites, which I plan to examine using simulations. Overall, this work justifies our method to reduce and compress 4D-STEM data in a physically meaningful and interpretable manner. | URI: | https://scholarbank.nus.edu.sg/handle/10635/239116 |
Appears in Collections: | Master's Theses (Open) |
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