Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing.,Advanced Materials

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Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing.
Advanced Materials ( IF 27.4 ) Pub Date : 2019-12-18 , DOI: 10.1002/adma.201905764
Jiankun Li ₁ , Chen Ge _{1,

2,

3} , Jianyu Du ₁ , Can Wang _{1,

2,

4} , Guozhen Yang ₁ , Kuijuan Jin _{1,

2,

4}

Affiliation

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.

中文翻译：

高性能神经形态计算的可再现超薄铁电域切换。

由人工突触和神经网络算法组成的神经形态计算为克服当前计算体系结构的固有局限性提供了一种有前途的方法。可以精确模拟生物突触的突触可塑性的电子设备的发展促进了神经形态计算的研究热潮。据报道，可以使用坚固的铁电隧道结来设计高性能的电子突触。这些器件通过逐步铁电畴切换，具有许多可重现状态（≈200），具有出色的忆阻器功能。可以通过精细调整电子突触中施加的脉冲参数来模拟短期和长期可塑性。模拟电导开关具有较高的线性度和对称性，且开关变化较小。

更新日期：2020-02-18

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