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Nat Commun. 2017 Apr 3;8:14736. doi: 10.1038/ncomms14736.

Learning through ferroelectric domain dynamics in solid-state synapses.

Author information

1
Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay, Palaiseau 91767, France.
2
University of Bordeaux, IMS, UMR 5218, Talence F-33405, France.
3
Department of Physics and Institute for Nanoscience and Engineering, University of Arkansas Fayetteville, Arkansas 72701, USA.
4
Centre de Nanosciences et de Nanotechnologies, CNRS, Univ. Paris Sud, Université Paris-Saclay, C2N-Orsay, Orsay cedex 91405, France.
5
Thales Research and Technology, 1 Avenue Augustin Fresnel, Campus de I'Ecole Polytechnique, Palaiseau 91767, France.

Abstract

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

PMID:
28368007
PMCID:
PMC5382254
DOI:
10.1038/ncomms14736
[Indexed for MEDLINE]
Free PMC Article

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