Blind identification of magnetic signals in electron magnetic chiral dichroism using independent component analysis

Jakob Spiegelberg; Dongsheng Song; Rafal E Dunin-Borkowski; Jing Zhu; Ján Rusz

doi:10.1016/j.ultramic.2018.08.021

Blind identification of magnetic signals in electron magnetic chiral dichroism using independent component analysis

Ultramicroscopy. 2018 Dec:195:129-135. doi: 10.1016/j.ultramic.2018.08.021. Epub 2018 Aug 27.

Authors

Jakob Spiegelberg¹, Dongsheng Song², Rafal E Dunin-Borkowski³, Jing Zhu⁴, Ján Rusz⁵

Affiliations

¹ Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden. Electronic address: [email protected].
² National Center for Electron Microscopy in Beijing, Key Laboratory of Advanced Materials (MOE) and The State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China; Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, D-52425 Jülich, Germany.
³ Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, D-52425 Jülich, Germany.
⁴ National Center for Electron Microscopy in Beijing, Key Laboratory of Advanced Materials (MOE) and The State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China.
⁵ Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.

PMID: 30237143
DOI: 10.1016/j.ultramic.2018.08.021

Abstract

Electron magnetic chiral dichroism (EMCD) is a promising technique to investigate local magnetic structures in the electron microscope. However, recognition of the EMCD signal, or also finding optimal parameter settings for given materials and sample orientations typically requires extensive simulations to aid the experiment. Here, we discuss how modern data processing techniques, in particular independent component analysis, can be used to identify magnetic signals in an unsupervised manner from energy filtered transmission electron microscopy (EFTEM) images. On the background of the recent advent of 4D scanning transmission electron microscopy, we discuss how this data processing may enable simultaneous tracking of all three spatial components of the magnetic momenta for arbitrary materials and several sample orientations without the previous need of complementary simulations.

Keywords: 4D STEM; Blind Source Separation; EMCD; ICA.

Publication types

Research Support, Non-U.S. Gov't