Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-28T13:04:57.452Z Has data issue: false hasContentIssue false
  • English
  • Français

Materials and process discovery by correlated STEM imaging and spectroscopy with electrical testing

Published online by Cambridge University Press:  30 July 2021

Andrew Wagner ,
John Nugent  and
Kumar Virwani
Andrew Wagner
Affiliation:
Intel Corporation, Beaverton, Oregon, United States
John Nugent
Affiliation:
Intel Corporation, United States
Kumar Virwani
Affiliation:
Intel Corporation, United States

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. 2758 - 2761
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Horwath, J., Zakharov, D., Megret, R. and Stach, E., "Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images," npj Comput Mater, pp. 6, 108, 2020.Google Scholar
Vasudevan, R., Ziatdinov, M., Vlcek, L. and Kalinin, S., "Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality," npj Comput Mater, pp. 7, 16, 2021.Google Scholar