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Learning through ferroelectric domain dynamics in solid-state synapses

Nature Communications2017Vol. 8(1), pp. 14736–14736

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Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, S. Fusil, Stéphanie Girod, Cécile Carrétéro, K. Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibès, A. 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.

Citations Over TimeTop 1% of 2017 papers

Abstract

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