Learning through ferroelectric domain dynamics in solid-state synapses
Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2017-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/ncomms14736 |
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doaj-4eca89cea8fb41d595066d4cec248c7a2021-05-11T07:19:08ZengNature Publishing GroupNature Communications2041-17232017-04-01811710.1038/ncomms14736Learning through ferroelectric domain dynamics in solid-state synapsesSören Boyn0Julie Grollier1Gwendal Lecerf2Bin Xu3Nicolas Locatelli4Stéphane Fusil5Stéphanie Girod6Cécile Carrétéro7Karin Garcia8Stéphane Xavier9Jean Tomas10Laurent Bellaiche11Manuel Bibes12Agnès Barthélémy13Sylvain Saïghi14Vincent Garcia15Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUniversity of Bordeaux, IMS, UMR 5218Department of Physics and Institute for Nanoscience and Engineering, University of Arkansas FayettevilleCentre de Nanosciences et de Nanotechnologies, CNRS, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayThales Research and TechnologyUniversity of Bordeaux, IMS, UMR 5218Department of Physics and Institute for Nanoscience and Engineering, University of Arkansas FayettevilleUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUnité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayUniversity of Bordeaux, IMS, UMR 5218Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-SaclayAccurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning and retrieving patterns in a neural system.https://doi.org/10.1038/ncomms14736 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia |
| spellingShingle |
Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia Learning through ferroelectric domain dynamics in solid-state synapses Nature Communications |
| author_facet |
Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia |
| author_sort |
Sören Boyn |
| title |
Learning through ferroelectric domain dynamics in solid-state synapses |
| title_short |
Learning through ferroelectric domain dynamics in solid-state synapses |
| title_full |
Learning through ferroelectric domain dynamics in solid-state synapses |
| title_fullStr |
Learning through ferroelectric domain dynamics in solid-state synapses |
| title_full_unstemmed |
Learning through ferroelectric domain dynamics in solid-state synapses |
| title_sort |
learning through ferroelectric domain dynamics in solid-state synapses |
| publisher |
Nature Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2017-04-01 |
| description |
Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning and retrieving patterns in a neural system. |
| url |
https://doi.org/10.1038/ncomms14736 |
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