Learning through ferroelectric domain dynamics in solid-state synapses

Boyn, Soren; Grollier, Julie; Lecerf, Gwendal; Xu, Bin; Locatelli, Nicolas; Fusil, Stephane; Girod, Stephanie; Carretero, Cecile; Garcia, Karin; Xavier, Stephane; Tomas, Jean; Bellaiche, Laurent; Bibes, Manuel; Barthelemy, Agnes; Saighi, Sylvain; Garcia, Vincent

doi:10.1038/ncomms14736

Learning through ferroelectric domain dynamics in solid-state synapses

Journal Article · Mon Apr 03 00:00:00 EDT 2017 · Nature Communications

DOI:https://doi.org/10.1038/ncomms14736· OSTI ID:1367544

Boyn, Soren ^[1]; Grollier, Julie ^[2]; Lecerf, Gwendal ^[3]; ^[4]; ^[5]; Fusil, Stephane ^[2]; Girod, Stephanie ^[6]; Carretero, Cecile ^[2]; Garcia, Karin ^[2]; Xavier, Stephane ^[7]; Tomas, Jean ^[3]; Bellaiche, Laurent ^[4]; Bibes, Manuel ^[2]; Barthelemy, Agnes ^[2]; Saighi, Sylvain ^[3]; Garcia, Vincent ^[2]

Univ. Paris-Saclay, Palaiseau (France); ETH Zurich, Zurich (Switzerland); Univ Paris Sud, Univ Paris Saclay, CNRS, Unite Mixte Phys, F-91767 Palaiseau, France
Univ. Paris-Saclay, Palaiseau (France)
Univ. of Bordeaux, Talence (France)
Univ. of Arkansas, Fayetteville, AR (United States)
Univ. Paris-Saclay, Orsay Cedex (France)
Univ. Paris-Saclay, Palaiseau (France); Luxembourg Institute of Science and Technology (LIST), Belvaux (Luxembourg)
Campus de l'Ecole Polytechnique, Palaiseau (France)

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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Arkansas, Fayetteville, AR (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); Defense Advanced Research Projects Agency (DARPA); European Union (EU); European Research Council (ERC)

Grant/Contract Number:: SC0002220

OSTI ID:: 1367544

Journal Information:: Nature Communications, Journal Name: Nature Communications Vol. 8; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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