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Revealing ferroelectric switching character using deep recurrent neural networks.

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.

SUBMITTER: Agar JC

PROVIDER: S-EPMC6805893 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Revealing ferroelectric switching character using deep recurrent neural networks.

Agar Joshua C JC Naul Brett B Pandya Shishir S van der Walt Stefan S Maher Joshua J Ren Yao Y Chen Long-Qing LQ Kalinin Sergei V SV Vasudevan Rama K RK Cao Ye Y Bloom Joshua S JS Martin Lane W LW

Nature communications 20191022 1

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 PbZr<sub>0 ...[more]

PMID: 31641122

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Dataset Information

Revealing ferroelectric switching character using deep recurrent neural networks.

Publications

Revealing ferroelectric switching character using deep recurrent neural networks.

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