Learning through ferroelectric domain dynamics in solid-state synapses.
Learning through ferroelectric domain dynamics in solid-state synapses.
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DOI：
10.1038/ncomms14736
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发表时间：
2017-04-03
影响因子：
16.6
通讯作者：
Garcia V
中科院分区：
文献类型：
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作者：
Boyn S;Grollier J;Lecerf G;Xu B;Locatelli N;Fusil S;Girod S;Carrétéro C;Garcia K;Xavier S;Tomas J;Bellaiche L;Bibes M;Barthélémy A;Saïghi S;Garcia V
在大脑中，学习是通过突触重新配置连接神经元的强度（突触可塑性）的能力来实现的。在被称为忆阻器的有前途的固态突触中，电导可以通过电压脉冲进行微调，并根据称为尖峰定时依赖可塑性（STDP）的生物学习规则进行进化。未来的神经形态结构将包括数十亿个这样的纳米突触，这需要对负责可塑性的物理机制有清楚的了解。在这里，我们报告基于铁电隧道结的突触，并表明STDP可以利用非均匀极化开关。通过结合扫描探针成像，电输运和原子尺度的分子动力学，我们表明，电导的变化可以模拟成核为主的反转域。基于这个物理模型，我们的模拟表明，铁电纳米突触阵列可以自主学习以可预测的方式识别模式，为尖峰神经网络的无监督学习开辟了道路。忆阻器动力学的精确建模对于人工神经网络中自主学习的发展至关重要。通过对铁电忆阻器中极化切换过程的理论和实验研究，Boyn等人建立了一个能够在神经系统中学习和检索模式的模型。
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, Boyn et al. establish a model that enables learning and retrieving patterns in a neural system.