来自法国国家科学研究中心的研究人员通过研究确定可以通过铁电忆阻器内的固有非均匀极化开关来实现STDP，相关工作发表在知名期刊《Nature Communications》上 ...
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摘要 在大脑中，学习是通过突触重新配置连接神经元的强度来实现的(突触可塑性)。在忆阻器固态突触中，电导可以被电压脉冲精确地调节，并根据一种被称为尖峰时间依赖性可塑性(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这样的纳米合成物，这需要对可塑性的物理机制有一个清晰的理解。在这里，我们报告了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调控。通过联合扫描探针成像，电输运和原子尺度的分子动力学，我们证明了电导的变化可以通过成核主导的畴反转来建模。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并以可预测的方式识别模式，这项工作开辟了通向脉冲神经网络中无监督学习的道路。 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. |
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