Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Ludacka, Ursula; He, Jiali; Qin, Shuyu; Zahn, Manuel; Christiansen, Emil Frang; Hunnestad, Kasper A.; Zhang, Xinqiao; Yan, Zewu; Bourret, Edith; Kézsmárki, István; van Helvoort, Antonius J.; Agar, Joshua; Meier, Dennis

doi:10.1038/s41524-024-01265-y

Title: Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Journal Article · 18 May 2024 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-024-01265-y · OSTI ID:2406227

Ludacka, Ursula ^[1]; He, Jiali ^[1]; Qin, Shuyu ^[2];

^[3]; Christiansen, Emil Frang ^[1];

^[1]; Zhang, Xinqiao ^[4]; Yan, Zewu ^[5]; Bourret, Edith ^[6]; Kézsmárki, István ^[7];

^[1];

^[2];

^[1]

Norwegian Univ. of Science and Technology, Trondheim (Norway)
Lehigh Univ., Bethlehem, PA (United States); Drexel Univ., Philadelphia, PA (United States)
Norwegian Univ. of Science and Technology, Trondheim (Norway); Univ. of Augsburg (Germany)
Drexel Univ., Philadelphia, PA (United States)
Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of Augsburg (Germany)

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O₃. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); European Research Council (ERC); US Army Research Office (ARO); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2406227

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 10; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (50)

Data from Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction Ludacka, Ursula; He, Jiali; Qin, Shuyu Zenodo https://doi.org/10.5281/zenodo.7837986	dataset	January 2023
m3-learning/m3_learning: STEM-AE Agar, Joshua C. https://doi.org/10.5281/zenodo.7844268	software	April 2023
Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr _0.2 Ti _0.8 O ₃ Thin Films Agar, Joshua C.; Cao, Ye; Naul, Brett Advanced Materials, Vol. 30, Issue 28 https://doi.org/10.1002/adma.201800701	journal	May 2018
Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films Qin, Shuyu; Guo, Yichen; Kaliyev, Alibek T. Advanced Materials, Vol. 34, Issue 47 https://doi.org/10.1002/adma.202202814	journal	October 2022
Automated Experiments of Local Non‐Linear Behavior in Ferroelectric Materials Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K. Small, Vol. 18, Issue 48 https://doi.org/10.1002/smll.202204130	journal	October 2022
Growth of high-quality hexagonal ErMnO3 single crystals by the pressurized floating-zone method Yan, Z.; Meier, D.; Schaab, J. Journal of Crystal Growth, Vol. 409 https://doi.org/10.1016/j.jcrysgro.2014.10.006	journal	January 2015
Position averaged convergent beam electron diffraction: Theory and applications LeBeau, James M.; Findlay, Scott D.; Allen, Leslie J. Ultramicroscopy, Vol. 110, Issue 2 https://doi.org/10.1016/j.ultramic.2009.10.001	journal	January 2010
Sample preparation for atomic-resolution STEM at low voltages by FIB Schaffer, Miroslava; Schaffer, Bernhard; Ramasse, Quentin Ultramicroscopy, Vol. 114 https://doi.org/10.1016/j.ultramic.2012.01.005	journal	March 2012
Efficient phase contrast imaging in STEM using a pixelated detector. Part 1: Experimental demonstration at atomic resolution Pennycook, Timothy J.; Lupini, Andrew R.; Yang, Hao Ultramicroscopy, Vol. 151, p. 160-167 https://doi.org/10.1016/j.ultramic.2014.09.013	journal	April 2015
Efficient phase contrast imaging in STEM using a pixelated detector. Part II: Optimisation of imaging conditions Yang, Hao; Pennycook, Timothy J.; Nellist, Peter D. Ultramicroscopy, Vol. 151 https://doi.org/10.1016/j.ultramic.2014.10.013	journal	April 2015
Accuracy and precision of thickness determination from position-averaged convergent beam electron diffraction patterns using a single-parameter metric Pollock, J. A.; Weyland, M.; Taplin, D. J. Ultramicroscopy, Vol. 181 https://doi.org/10.1016/j.ultramic.2017.05.001	journal	October 2017
Patterned probes for high precision 4D-STEM bragg measurements Zeltmann, Steven E.; Müller, Alexander; Bustillo, Karen C. Ultramicroscopy, Vol. 209 https://doi.org/10.1016/j.ultramic.2019.112890	journal	February 2020
Cepstral scanning transmission electron microscopy imaging of severe lattice distortions Shao, Yu-Tsun; Yuan, Renliang; Hsiao, Haw-Wen Ultramicroscopy, Vol. 231 https://doi.org/10.1016/j.ultramic.2021.113252	journal	December 2021
Xe+ FIB Milling and Measurement of Amorphous Silicon Damage Kelley, R. D.; Song, K.; Van Leer, B. Microscopy and Microanalysis, Vol. 19, Issue S2 https://doi.org/10.1017/S1431927613006302	journal	August 2013
Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond Ophus, Colin Microscopy and Microanalysis, Vol. 25, Issue 3 https://doi.org/10.1017/S1431927619000497	journal	May 2019
A Python Based Open-source Multislice Simulation Package for Transmission Electron Microscopy Brown, Hamish; Pelz, Phillipp; Ophus, Colin Microscopy and Microanalysis, Vol. 26, Issue S2 https://doi.org/10.1017/S1431927620023326	journal	July 2020
Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data Groschner, Catherine K.; Choi, Christina; Scott, Mary C. Microscopy and Microanalysis, Vol. 27, Issue 3 https://doi.org/10.1017/S1431927621000386	journal	May 2021
Real-Time Integration Center of Mass (riCOM) Reconstruction for 4D STEM Yu, Chu-Ping; Friedrich, Thomas; Jannis, Daen Microscopy and Microanalysis, Vol. 28, Issue 5 https://doi.org/10.1017/S1431927622000617	journal	October 2022
4D-STEM of Beam-Sensitive Materials Bustillo, Karen C.; Zeltmann, Steven E.; Chen, Min Accounts of Chemical Research, Vol. 54, Issue 11 https://doi.org/10.1021/acs.accounts.1c00073	journal	May 2021
Quantifying the Local Structure of Nanocrystals, Glasses, and Interfaces Using TEM-Based Diffraction Ehrhardt, Karen M.; Radomsky, Rebecca C.; Warren, Scott C. Chemistry of Materials, Vol. 33, Issue 23 https://doi.org/10.1021/acs.chemmater.1c03017	journal	November 2021
Quantitative Characterization of Nanometer-Scale Electric Fields via Momentum-Resolved STEM Beyer, Andreas; Munde, Manveer Singh; Firoozabadi, Saleh Nano Letters, Vol. 21, Issue 5 https://doi.org/10.1021/acs.nanolett.0c04544	journal	February 2021
Measuring Lattice Strain in Three Dimensions through Electron Microscopy Goris, Bart; De Beenhouwer, Jan; De Backer, Annick Nano Letters, Vol. 15, Issue 10 https://doi.org/10.1021/acs.nanolett.5b03008	journal	September 2015
Topological Defects in Hexagonal Manganites: Inner Structure and Emergent Electrostatics Holtz, Megan E.; Shapovalov, Konstantin; Mundy, Julia A. Nano Letters, Vol. 17, Issue 10 https://doi.org/10.1021/acs.nanolett.7b01288	journal	September 2017
Collective Magnetism at Multiferroic Vortex Domain Walls Geng, Yanan; Lee, N.; Choi, Y. J. Nano Letters, Vol. 12, Issue 12 https://doi.org/10.1021/nl301432z	journal	February 2012
Structural domain walls in polar hexagonal manganites Kumagai, Yu; Spaldin, Nicola A. Nature Communications, Vol. 4, Issue 1 https://doi.org/10.1038/ncomms2545	journal	February 2013
The origin of ferroelectricity in magnetoelectric YMnO3 Van Aken, Bas B.; Palstra, Thomas T. M.; Filippetti, Alessio Nature Materials, Vol. 3, Issue 3 https://doi.org/10.1038/nmat1080	journal	February 2004
Insulating interlocked ferroelectric and structural antiphase domain walls in multiferroic YMnO3 Choi, T.; Horibe, Y.; Yi, H. T. Nature Materials, Vol. 9, Issue 3 https://doi.org/10.1038/nmat2632	journal	February 2010
Revealing ferroelectric switching character using deep recurrent neural networks Agar, Joshua C.; Naul, Brett; Pandya, Shishir Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12750-0	journal	October 2019
Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls Holstad, Theodor S.; Ræder, Trygve M.; Evans, Donald M. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00426-z	journal	October 2020
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering Oxley, Mark P.; Ziatdinov, Maxim; Dyck, Ondrej npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00527-3	journal	May 2021
Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns Munshi, Joydeep; Rakowski, Alexander; Savitzky, Benjamin H. npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00939-9	journal	December 2022
Machine learning for automated experimentation in scanning transmission electron microscopy Kalinin, Sergei V.; Mukherjee, Debangshu; Roccapriore, Kevin npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01142-0	journal	December 2023
Conductivity control via minimally invasive anti-Frenkel defects in a functional oxide Evans, Donald M.; Holstad, Theodor S.; Mosberg, Aleksander B. Nature Materials, Vol. 19, Issue 11 https://doi.org/10.1038/s41563-020-0765-x	journal	August 2020
Phase contrast scanning transmission electron microscopy imaging of light and heavy atoms at the limit of contrast and resolution Yücelen, Emrah; Lazić, Ivan; Bosch, Eric G. T. Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-20377-2	journal	February 2018
Few-nm tracking of current-driven magnetic vortex orbits using ultrafast Lorentz microscopy Möller, Marcel; Gaida, John H.; Schäfer, Sascha Communications Physics, Vol. 3, Issue 1 https://doi.org/10.1038/s42005-020-0301-y	journal	February 2020
Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images Lu, Shizhao; Montz, Brian; Emrick, Todd Digital Discovery, Vol. 1, Issue 6 https://doi.org/10.1039/D2DD00066K	journal	January 2022
Electrostatic topology of ferroelectric domains in YMnO3 Jungk, Tobias; Hoffmann, Ákos; Fiebig, Manfred Applied Physics Letters, Vol. 97, Issue 1 https://doi.org/10.1063/1.3460286	journal	July 2010
Atomic-resolution chemical imaging of oxygen local bonding environments by electron energy loss spectroscopy Mundy, Julia A.; Mao, Qingyun; Brooks, Charles M. Applied Physics Letters, Vol. 101, Issue 4 https://doi.org/10.1063/1.4737208	journal	July 2012
Measuring temperature-dependent thermal diffuse scattering using scanning transmission electron microscopy Wehmeyer, Geoff; Bustillo, Karen C.; Minor, Andrew M. Applied Physics Letters, Vol. 113, Issue 25 https://doi.org/10.1063/1.5066111	journal	December 2018
Unsupervised machine learning discovery of structural units and transformation pathways from imaging data Kalinin, Sergei V.; Dyck, Ondrej; Ghosh, Ayana APL Machine Learning, Vol. 1, Issue 2 https://doi.org/10.1063/5.0147316	journal	June 2023
Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning Kalinin, Sergei V.; Steffes, James J.; Liu, Yongtao Nanotechnology, Vol. 33, Issue 5 https://doi.org/10.1088/1361-6528/ac2f5b	journal	November 2021
Electronic bulk and domain wall properties in B -site doped hexagonal ErMnO 3 Holstad, T. S.; Evans, D. M.; Ruff, A. Physical Review B, Vol. 97, Issue 8 https://doi.org/10.1103/PhysRevB.97.085143	journal	February 2018
Hexagonal YMnO ₃ Aken, Bas B. van; Meetsma, Auke; Palstra, Thomas T. M. Acta Crystallographica Section C Crystal Structure Communications, Vol. 57, Issue 3 https://doi.org/10.1107/S0108270100015663	journal	March 2001
Three-dimensional nanostructure determination from a large diffraction data set recorded using scanning electron nanodiffraction Meng, Yifei; Zuo, Jian-Min IUCrJ, Vol. 3, Issue 5 https://doi.org/10.1107/S205225251600943X	journal	July 2016
Advanced preparation of plan-view specimens on a MEMS chip for in situ TEM heating experiments Minenkov, Alexey; Šantić, Natalija; Truglas, Tia MRS Bulletin, Vol. 47, Issue 4 https://doi.org/10.1557/s43577-021-00255-5	journal	March 2022
Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication Kalinin, Sergei V.; Ziatdinov, Maxim; Spurgeon, Steven R. MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00413-3	journal	September 2022
Applications and Techniques for Fast Machine Learning in Science Deiana, Allison McCarn; Tran, Nhan; Agar, Joshua Frontiers in Big Data, Vol. 5 https://doi.org/10.3389/fdata.2022.787421	journal	April 2022
STEM Tools for Semiconductor Characterization: Beyond High-Resolution Imaging de la Mata, María; Molina, Sergio I. Nanomaterials, Vol. 12, Issue 3 https://doi.org/10.3390/nano12030337	journal	January 2022
Data from Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction Ludacka, Ursula; He, Jiali; Qin, Shuyu Zenodo https://doi.org/10.5281/zenodo.7837986	dataset	January 2023
m3-learning/m3_learning: STEM-AE Agar, Joshua C. https://doi.org/10.5281/zenodo.7844268	software	April 2023

Similar Records

Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

Journal Article · 2020 · npj Computational Materials · OSTI ID:1764554

Holstad, Theodor S.; Ræder, Trygve M.; Evans, Donald M.; +10 more

Dynamic Conductivity of Ferroelectric Domain Walls in BiFeO₃

Journal Article · 2011 · Nano Letters · OSTI ID:1337809

Maksymovych, Peter; Seidel, Jan; Chu, Ying Hao; +5 more

Light-activated Gigahertz Ferroelectric Domain Dynamics

Journal Article · 2018 · Physical Review Letters · OSTI ID:1426228

Akamatsu, Hirofumii; Yuan, Yakun; Stoica, Vladimir A.; +10 more

Related Subjects

36 MATERIALS SCIENCE
Ferroelectrics and multiferroics
imaging techniques

Title: Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Citation Formats

References (50)

Similar Records

Related Subjects