Revealing ferroelectric switching character using deep recurrent neural networks

Joshua C. Agar (), Brett Naul, Shishir Pandya, Stefan Walt, Joshua Maher, Yao Ren, Long-Qing Chen, Sergei V. Kalinin, Rama K. Vasudevan, Ye Cao, Joshua S. Bloom and Lane W. Martin ()
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Joshua C. Agar: University of California, Berkeley
Brett Naul: University of California, Berkeley
Shishir Pandya: University of California, Berkeley
Stefan Walt: University of California, Berkeley
Joshua Maher: University of California, Berkeley
Yao Ren: The University of Texas at Arlington
Long-Qing Chen: Pennsylvania State University
Sergei V. Kalinin: Oak Ridge National Laboratory
Rama K. Vasudevan: Oak Ridge National Laboratory
Ye Cao: The University of Texas at Arlington
Joshua S. Bloom: University of California, Berkeley
Lane W. Martin: University of California, Berkeley

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material’s physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.

Date: 2019
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DOI: 10.1038/s41467-019-12750-0

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