Revealing ferroelectric switching character using deep recurrent neural networks

Joshua C. Agar; Brett Naul; Shishir Pandya; Stefan van der Walt; Joshua Maher; Yao Ren; Long-Qing Chen; Sergei V. Kalinin; Rama K. Vasudevan; Ye Cao; Joshua S. Bloom; Lane W. Martin

doi:10.1038/s41467-019-12750-0

Nature Communications (Oct 2019)

Revealing ferroelectric switching character using deep recurrent neural networks

Joshua C. Agar,
Brett Naul,
Shishir Pandya,
Stefan van der Walt,
Joshua Maher,
Yao Ren,
Long-Qing Chen,
Sergei V. Kalinin,
Rama K. Vasudevan,
Ye Cao,
Joshua S. Bloom,
Lane W. Martin

Affiliations

Joshua C. Agar: Department of Materials Science and Engineering, University of California, Berkeley
Brett Naul: Department of Astronomy, University of California, Berkeley
Shishir Pandya: Department of Materials Science and Engineering, University of California, Berkeley
Stefan van der Walt: Berkeley Institute of Data Science, University of California, Berkeley
Joshua Maher: Department of Materials Science and Engineering, University of California, Berkeley
Yao Ren: Department of Materials Science and Engineering, The University of Texas at Arlington
Long-Qing Chen: Department of Materials Science and Engineering, Pennsylvania State University
Sergei V. Kalinin: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Rama K. Vasudevan: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Ye Cao: Department of Materials Science and Engineering, The University of Texas at Arlington
Joshua S. Bloom: Department of Astronomy, University of California, Berkeley
Lane W. Martin: Department of Materials Science and Engineering, University of California, Berkeley

DOI: https://doi.org/10.1038/s41467-019-12750-0
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 11

Abstract

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The scale and dimensionality of imaging data means information is commonly overlooked. Here, using recurrent neural networks we understand temporal dependencies in hyperspectral imagery, enabling the observation of differences in ferroelectric switching mechanisms in PbZr0.2Ti0.8O3 thin films due to formation of charged domain walls.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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