Revealing ferroelectric switching character using deep recurrent neural networks

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

doi:10.1038/s41467-019-12750-0

Title: 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 PbZr_0.2Ti_0.8O₃ 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.

Authors:

Agar, Joshua C. ^[1];

^[2]; Pandya, Shishir ^[2];

^[3]; Maher, Joshua ^[2]; Ren, Yao ^[4]; Chen, Long-Qing ^[5];

^[6];

^[6]; Cao, Ye ^[4]; Bloom, Joshua S. ^[2];

^[7]

Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lehigh Univ., Bethlehem, PA (United States)
Univ. of California, Berkeley, CA (United States)
Univ. of California, Berkeley, CA (United States). Berkeley Inst. of Data Science
Univ. of Texas, Arlington, TX (United States)
Pennsylvania State Univ., University Park, PA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Tue Oct 22 00:00:00 EDT 2019

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

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; National Science Foundation (NSF); Gordon and Betty Moore Foundation (GBMF); US Army Research Office (ARO); Alfred P. Sloan Foundation; Nano/Human Interfaces Presidential Initiative

OSTI Identifier:: 1580953

Grant/Contract Number:: AC02-05CH11231; DMR-1708615; DMR-1744213; TRIPODS + X:RES-1839234; 1251274; W911NF-14-1-0104; GBMF3834; 2013-10-27

Resource Type:: Accepted Manuscript

Journal Name:: Nature Communications

Additional Journal Information:: Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE

Citation Formats


                    Agar, Joshua C., Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Maher, Joshua, Ren, Yao, Chen, Long-Qing, Kalinin, Sergei V., Vasudevan, Rama K., Cao, Ye, Bloom, Joshua S., and Martin, Lane W. Revealing ferroelectric switching character using deep recurrent neural networks.  United States: N. p., 2019. 
Web.  doi:10.1038/s41467-019-12750-0.

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                    Agar, Joshua C., Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Maher, Joshua, Ren, Yao, Chen, Long-Qing, Kalinin, Sergei V., Vasudevan, Rama K., Cao, Ye, Bloom, Joshua S., & Martin, Lane W. Revealing ferroelectric switching character using deep recurrent neural networks.  United States.  https://doi.org/10.1038/s41467-019-12750-0

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                    Agar, Joshua C., Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Maher, Joshua, Ren, Yao, Chen, Long-Qing, Kalinin, Sergei V., Vasudevan, Rama K., Cao, Ye, Bloom, Joshua S., and Martin, Lane W. Tue .  
"Revealing ferroelectric switching character using deep recurrent neural networks".  United States.  https://doi.org/10.1038/s41467-019-12750-0.  https://www.osti.gov/servlets/purl/1580953.

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@article{osti_1580953,

  title        = {Revealing ferroelectric switching character using deep recurrent neural networks},

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

  abstractNote = {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.},

  doi          = {10.1038/s41467-019-12750-0},

  journal      = {Nature Communications},

  number       = 1,

  volume       = 10,

  place        = {United States},

  year         = {Tue Oct 22 00:00:00 EDT 2019},

  month        = {Tue Oct 22 00:00:00 EDT 2019}

}

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