Revealing ferroelectric switching character using deep recurrent neural networks
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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-12750-0 |
| _version_ | 1818405327316451328 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Joshua C. Agar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T08:54:17Z |
| format | Article |
| id | doaj.art-6c60e037e5014ff79246798588ae89fa |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-14T08:54:17Z |
| publishDate | 2019-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-6c60e037e5014ff79246798588ae89fa2022-12-21T23:08:58ZengNature PortfolioNature Communications2041-17232019-10-0110111110.1038/s41467-019-12750-0Revealing ferroelectric switching character using deep recurrent neural networksJoshua C. Agar0Brett Naul1Shishir Pandya2Stefan van der Walt3Joshua Maher4Yao Ren5Long-Qing Chen6Sergei V. Kalinin7Rama K. Vasudevan8Ye Cao9Joshua S. Bloom10Lane W. Martin11Department of Materials Science and Engineering, University of California, BerkeleyDepartment of Astronomy, University of California, BerkeleyDepartment of Materials Science and Engineering, University of California, BerkeleyBerkeley Institute of Data Science, University of California, BerkeleyDepartment of Materials Science and Engineering, University of California, BerkeleyDepartment of Materials Science and Engineering, The University of Texas at ArlingtonDepartment of Materials Science and Engineering, Pennsylvania State UniversityCenter for Nanophase Materials Sciences, Oak Ridge National LaboratoryCenter for Nanophase Materials Sciences, Oak Ridge National LaboratoryDepartment of Materials Science and Engineering, The University of Texas at ArlingtonDepartment of Astronomy, University of California, BerkeleyDepartment of Materials Science and Engineering, University of California, BerkeleyThe 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.https://doi.org/10.1038/s41467-019-12750-0 |
| spellingShingle | 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 Revealing ferroelectric switching character using deep recurrent neural networks Nature Communications |
| title | Revealing ferroelectric switching character using deep recurrent neural networks |
| title_full | Revealing ferroelectric switching character using deep recurrent neural networks |
| title_fullStr | Revealing ferroelectric switching character using deep recurrent neural networks |
| title_full_unstemmed | Revealing ferroelectric switching character using deep recurrent neural networks |
| title_short | Revealing ferroelectric switching character using deep recurrent neural networks |
| title_sort | revealing ferroelectric switching character using deep recurrent neural networks |
| url | https://doi.org/10.1038/s41467-019-12750-0 |
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