Learning through ferroelectric domain dynamics in solid-state synapses

被引:466
作者
Boyn, Soeren [1 ,7 ]
Grollier, Julie [1 ]
Lecerf, Gwendal [2 ]
Xu, Bin [3 ,4 ]
Locatelli, Nicolas [5 ]
Fusil, Stephane [1 ]
Girod, Stephanie [1 ,8 ]
Carretero, Cecile [1 ]
Garcia, Karin [1 ]
Xavier, Stephane [6 ]
Tomas, Jean [2 ]
Bellaiche, Laurent [3 ,4 ]
Bibes, Manuel [1 ]
Barthelemy, Agnes [1 ]
Saighi, Sylvain [2 ]
Garcia, Vincent [1 ]
机构
[1] Univ Paris Sud, Univ Paris Saclay, CNRS, Unite Mixte Phys, F-91767 Palaiseau, France
[2] Univ Bordeaux, IMS, UMR 5218, F-33405 Talence, France
[3] Univ Arkansas, Dept Phys, Fayetteville, AR 72701 USA
[4] Univ Arkansas, Inst Nanosci & Engn, Fayetteville, AR 72701 USA
[5] Univ Paris Sud, Univ Paris Saclay, CNRS, C2N Orsay,Ctr Nanosci & Nanotechnol, F-91405 Orsay, France
[6] Thales Res & Technol, 1 Ave Augustin Fresnel,Campus Ecole Polytech, F-91767 Palaiseau, France
[7] Swiss Fed Inst Technol, Electrochem Mat, CH-8092 Zurich, Switzerland
[8] LIST, Mat Res & Technol Dept, 41 Rue Brill, L-4422 Belvaux, Luxembourg
基金
欧洲研究理事会; 欧盟地平线“2020”;
关键词
MEMRISTIVE DEVICES; MEMORY DEVICE; PLASTICITY;
D O I
10.1038/ncomms14736
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:7
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