Learning through ferroelectric domain dynamics in solid-state synapses

By S. Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stephane Fusil, S. Girod, C. Carrétéro, K. Garcı́a, S. Xavier and 6 more. Nature Communications, 2017

519 citations recorded by the source corpus.

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. 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.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/33d86819ef59d145c8a406328a4ec28c1f9fd7ec

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits (2017, background): “Various types of emerging nonvolatile memories are now being actively investigated for their use in fast and energy-efficient neuromorphic networks 19-41 19-41 .”
Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, background): “In recent years, 2 T memristors based on ferroelectric materials exhibit a broad prospect in fields such as AI and neural network computing [76, 77].”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “…of STDP is in terms of chip area costly for the presented neuromorphic systems, novel electronic components, so called memristors, may allow for much higher densities of plastic synapses (Jo et al., 2010; Saïghi et al., 2015; Boyn et al., 2017; Burr et al., 2017; Pedretti et al., 2017).”