Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-23T13:07:38.206Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterization of III/V Semiconductors on Silicon by Analyzing 4D-STEM Data with Convolutional Neural Networks

Published online by Cambridge University Press:  30 July 2021

Damien Heimes ,
Jonas Scheunert ,
Andreas Beyer ,
Jürgen Belz ,
Saleh Firoozabadi  and
Kerstin Volz
Damien Heimes
Affiliation:
Materials Science Centre and Department of Physics, Philipps University Marburg, Hans-Meerwein-Straße 6, Marburg, 35032, Germany, Germany
Jonas Scheunert
Affiliation:
STRL, University of Marburg, Germany
Andreas Beyer
Affiliation:
Materials Science Centre and Department of Physics, Philipps University Marburg, Hans-Meerwein-Straße 6, Marburg, 35032, Germany, Germany
Jürgen Belz
Affiliation:
STRL, University of Marburg, Germany
Saleh Firoozabadi
Affiliation:
Materials Science Centre and Department of Physics, Philipps University Marburg, Hans-Meerwein-Straße 6, Marburg, 35032, Germany, Marburg, Hessen, Germany
Kerstin Volz
Affiliation:
Materials Science Centre and Department of Physics, Philipps University Marburg, Hans-Meerwein-Straße 6, Marburg, 35032, Germany, Germany

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 450 - 452
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Beyer, A., & Volz, K. (2019). Advanced Electron Microscopy for III/V on Silicon Integration. Advanced Materials Interfaces, 6(12), 1801951.Google Scholar
Beyer, A., Munde, M. S., Firoozabadi, S., Heimes, D., Grieb, T., Rosenauer, A., Müller-Caspary, K., & Volz, K. Quantitative Characterization of Nanometer-Scale Electric Fields via Momentum-Resolved STEM. Nano Letters (accepted 2021-02-19).CrossRefGoogle Scholar
Gao, W., Addiego, C., Wang, H., Yan, X., Hou, Y., Ji, D., Heikes, C., Zhang, Y., Li, L., Huyan, H., Blum, T., Aoki, T., Nie, Y., Schlom, D. G., Wu, R., & Pan, X. (2019). Real-space charge-density imaging with sub-ångström resolution by four-dimensional electron microscopy. Nature, 575(7783), 480–484.CrossRefGoogle ScholarPubMed
Ophus, C. (2019). Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond. Microscopy and Microanalysis, 2019, 563–582.Google Scholar
Xu, W., & LeBeau, J. M. (2018). A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns. Ultramicroscopy, 188, 59–69.CrossRefGoogle ScholarPubMed
Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T., & Miller, B. D. (2019). Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning. Science Advances, 5(10), 1–10.CrossRefGoogle ScholarPubMed
Oelerich, J. O., Duschek, L., Belz, J., Beyer, A., Baranovskii, S. D., & Volz, K. (2017). STEMsalabim: A high-performance computing cluster friendly code for scanning transmission electron microscopy image simulations of thin specimens. Ultramicroscopy, 177, 91–96.CrossRefGoogle ScholarPubMed
Kirkland, E. J. (2020). Advanced Computing in Electron Microscopy: Third Edition. Springer Nature Switzerland AG.CrossRefGoogle Scholar