Learning through ferroelectric domain dynamics in solid-state synapses View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-04-03

AUTHORS

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

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. More... »

PAGES

14736

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ncomms14736

DOI

http://dx.doi.org/10.1038/ncomms14736

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084508653

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28368007


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