Key Laboratory of Optoelectronic Information Materials of Hebei Province, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, China.
Nanoscale. 2020 Nov 5;12(42):21913-21922. doi: 10.1039/d0nr03724a.
Catering to the general trend of artificial intelligence development, simulating humans' learning and thinking behavior has become the research focus. Second-order memristors, which are more analogous to biological synapses, are the most promising devices currently used in neuromorphic/brain-like computing. However, few second-order memristors based on two-dimensional (2D) materials have been reported, and the inherent bionic physics needs to be explored. In this work, a second-order memristor based on 2D SnSe films was fabricated by the pulsed laser deposition technique. The continuously adjustable conductance of Au/SnSe/NSTO structures was achieved by gradually switching the polarization of a ferroelectric SnSe layer. The experimental results show that the bio-synaptic functions, including spike-timing-dependent plasticity, short-term plasticity and long-term plasticity, can be simulated using this two-terminal devices. Moreover, stimulus pulses with nanosecond pulse duration were applied to the device to emulate rapid learning and long-term memory in the human brain. The observed memristive behavior is mainly attributed to the modulation of the width of the depletion layer and barrier height is affected, at the SnSe/NSTO interface, by the reversal of ferroelectric polarization of SnSe materials. The device energy consumption is as low as 66 fJ, being expected to be applied to miniaturized, high-density, low-power neuromorphic computing.
迎合人工智能发展的总体趋势,模拟人类的学习和思维行为已成为研究重点。二阶忆阻器更类似于生物突触,是目前在神经形态/类脑计算中最有前途的器件。然而,基于二维(2D)材料的二阶忆阻器报道较少,其内在的仿生物理特性有待进一步探索。本工作采用脉冲激光沉积技术制备了基于 2D SnSe 薄膜的二阶忆阻器。通过逐渐反转铁电 SnSe 层的极化,实现了 Au/SnSe/NSTO 结构的连续可调电导。实验结果表明,该二端器件可模拟生物突触功能,包括尖峰时间依赖可塑性、短期可塑性和长期可塑性。此外,该器件还施加了纳秒脉冲宽度的激励脉冲,以模拟人类大脑中的快速学习和长期记忆。观察到的忆阻行为主要归因于 SnSe/NSTO 界面处铁电极化反转对耗尽层宽度和势垒高度的调制。该器件的能量消耗低至 66 fJ,有望应用于小型化、高密度、低功耗的神经形态计算。