Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Adv Mater. 2020 Feb;32(7):e1905764. doi: 10.1002/adma.201905764. Epub 2019 Dec 18.
Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.
神经形态计算由人工突触和神经网络算法组成，为克服当前计算体系结构的固有局限性提供了一种很有前途的方法。能够准确模拟生物突触突触可塑性的电子器件的发展，促进了神经形态计算的研究热潮。据报道，稳健的铁电隧道结可用于设计高性能电子突触。这些器件通过逐渐的铁电畴切换显示出优异的忆阻器功能和许多可重复的状态（≈200）。通过精细调整电子突触中施加的脉冲参数，可以模拟短时间和长时间的可塑性。模拟电导切换具有高线性度和对称性，开关变化小。由这些突触器件构建的具有监督学习功能的模拟人工神经网络对混合国家标准与技术研究所 (MNIST) 手写识别数据集的分类准确率达到 96.4%。
