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兼具长时程可塑性与短时程可塑性的电子突触被认为是类脑计算系统的重要基础. 将一种新型二维材料MXene应用到忆阻器中, 制备了基于Cu/MXene/SiO2/W的仿神经突触忆阻器. 结果表明, Cu/MXene/SiO2/W忆阻器成功实现了稳定的双极性模拟阻态切换, 同时成功模拟了生物突触短时程可塑性的双脉冲易化功能和长时程可塑性的长期增强/抑制行为, 其中双脉冲易化的易化指数与脉冲间隔时间相关. Cu/MXene/SiO2/W忆阻器的突触仿生特性, 归功于MXene辅助的Cu离子电导丝形成与破灭的类突触响应机理. 由于Cu/MXene/SiO2/W忆阻器兼具长时程可塑性与短时程可塑性, 其在突触仿生电子学和类脑智能领域将会具有巨大的应用前景.Compared with conventional computation relying on the von Neumann architecture, brain-inspired computing has shown superior strength in various cognitive tasks. It has been generally accepted that information in the brain is represented and formed by vastly interconnected synapses. So the physical implementation of electronic synaptic devices is crucial to the development of brain-based computing systems. Among a large number of electronic synaptic devices, the memristors have attracted significant attention due to its simple structure and similarities to biological synapses. In this work, we first use two-dimensional material MXene as a resistive material and fabricate an electronic synapse based on a Cu/MXene/SiO2/W memristor. By using the unique properties of MXene, the conductance of the memristor can be modulated by the accumulation or reflux of Cu2+ at the physical switching layer, which can vividly simulate the mechanism of bio-synapses. Experimental results show that the Cu/MXene/SiO2/W memristor not only achieves stable bipolar analog resistance switching but also shows excellent long-term and short-term synaptic behaviors, including paired-pulse facilitation (PPF) and long-term potential/depression. By adjusting the pulse interval, the PPF index will change accordingly. In a biological system, the short-term plasticity is considered to be the key point for performing computational functions while the long-term plasticity is believed to underpin learning and memory functions. This work indicates that Cu/MXene/SiO2/W memristor with both long-term and short-term plasticity will have great application prospects for brain-inspired intelligence in the future.
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Keywords:
- MXene /
- memristor /
- ion diffusion /
- synaptic plasticity
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图 1 (a) Cu/MXene/SiO2/W忆阻器结构示意图; (b) MXene的SEM照片; (c) 器件电铸I-V 曲线; (d) 器件Set/Reset的I-V曲线
Fig. 1. (a) Device structures of the Cu/MXene/SiO2/W memristor; (b) SEM images of the MXene; (c) I-V curve of electroforming process; (d) I-V curve of Set/Reset process
图 2 (a) 连续正向电压扫描下模拟特性I-V曲线; (b) 正向扫描电导与扫描次数的关系; (c) 连续负向电压扫描下模拟特性I-V曲线; (d) 负向扫描电导与扫描次数的关系
Fig. 2. (a) Analog I-V curves under consecutive positive sweep voltage; (b) relationship between conductivity and scanning number under consecutive positive sweep voltage; (c) analog I-V curves under consecutive negative sweep voltage; (d) relationship between conductivity and scanning number under consecutive negative sweep voltage.
图 3 在连续正向和负向三角尖峰脉冲下, 器件电导的变化趋势
Fig. 3. Variation trend of conductance of the device with the continuous positive and negative voltage spike.
图 4 (a) 两个连续脉冲刺激作用下的PPF特性曲线; (b) PPF 指数与脉冲时间间隔的关系
Fig. 4. (a) PPF characteristic curve under two continuous pulse stimuli; (b) relationship between the PPF index and pulse interval.
图 5 Cu/MXene/SiO2/W忆阻器生物响应机理 (a)正偏压下Cu2+的扩散与迁移运动; (b)负偏压下Cu2+的扩散与迁移运动; (c)撤去偏压, 电导丝的自主破灭; (d)残余电导丝与新形成的电导丝
Fig. 5. Synapse-like mechanism of Cu/MXene/SiO2/W memristor: (a) Diffusion and migration of Cu2+ under positive voltage; (b) diffusion and migration of Cu2+ under negative voltage; (c) spontaneous rupture of conductive filament when the voltage is removed; (d) residual conductive filaments and newly formed conductive filaments.
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[1] Chen Y H, Yu H Y, Gong J D, Ma M X, Han H, Wei H H, Xu W T 2019 Nanotechnology 30 012001
Google Scholar
[2] Mead C 1990 Proc. IEEE 78 1629
[3] Zhao Y H, Jie J 2018 J. Nanosci. Nanotechnol. 18 8003
Google Scholar
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Google Scholar
Liang D K, Chen Y H, Xu W, Ji X C, Tong Y, Wu G D 2018 Acta Phys. Sin. 67 237302
Google Scholar
[5] Dmitri B S, Gregory S S, Duncan R S, Williams R S 2008 Nature 453 80
Google Scholar
[6] Jeong H, Shi L P 2019 J. Phys. D: Appl. Phys. 52 023003
[7] Waser R, Dittmann R, Staikov G, Kristof S 2009 Adv. Mater. 21 2632
Google Scholar
[8] 余志强, 刘敏丽, 郎建勋, 钱楷, 张昌华 2018 物理学报 67 157302
Google Scholar
Yu Z Q, Liu M L, Lang J X, Qian K, Zhang C H 2018 Acta Phys. Sin. 67 157302
Google Scholar
[9] Chang T, Jo S H, Lu W 2011 ACS Nano 5 7669
Google Scholar
[10] Kim M K, Lee J S 2018 ACS Nano 12 1680
[11] Hirano T 2018 Cerebellum 17 756
Google Scholar
[12] Wang C H, He W, Tong Y, Zhao R 2016 Sci. Rep. 6 22970
Google Scholar
[13] Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C, Zhu X J 2012 Adv. Funct. Mater. 22 2759
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[16] Sun D, Ye D L, Liu P, Tang Y G, Guo J, Wang L Z, Wang H Y 2018 Adv. Energy Mater. 8 1702383
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Wu Q T, Shi T, Zhao X L, Zhang X M, Wu F C, Cao R R, Long S B, Lv H B, Liu Q, Liu M 2017 Acta Phys. Sin. 66 217304
Google Scholar
[19] Liu C Y, Zhang Y X, Yang C P 2017 Sensors Mater. 30 463
Google Scholar
[20] Wang M, Cai S H, Pan C, Wang C Y, Lian X J, Zhuo Y, Xu K, Cao T J, Pan X Q, Wang B G, Liang S J, Yang J J, Wang P, Miao F 2018 Nat. Electron. 1 130
[21] Voigt C A, Ghidiu M, Natu V, Barsoum M W 2018 J. Phys. Chem. C 122 23172
[22] Shahzad F, Alhabeb M, Hatter C B, Anasori B, Hong S M, Koo C M, Gogotsi Y 2016 Science 353 1137
Google Scholar
[23] Wu Y T, Nie P, Wu L Y, Dou H, Zhang X G 2018 Chem. Eng. J. 334 932
[24] Jiang X T, Liu S X, Liang W Y, Luo S J, He Z L, Ge Y Q, Wang H D, Cao R, Zhang F, Wen Q, Li J Q, Bao Q L, Fan D Y, Zhang H 2018 Laser Photon. Rev. 12 1700229
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[26] Zhao X L, Ma J, Xiao X H, Liu Q, Shao L, Chen D, Liu S, Niu J B, Zhang X M, Wang Y, Cao R R, Wang W, Di Z F, Lv H B, Long S B, Liu M 2018 Adv. Mater. 30 1705193
[27] Scott T, Salvatore A, Woo P, Lee Y Y, Salvati E A, Della Valle A G 2018 J. Arthroplast. 33 1120
Google Scholar
[28] Tempia F, Hoxha E, Negro G, Alshammari M A, Alshammari T K, Panova-Elektronova N, Laezza F 2015 Front. Cell. Neurosci. 9 205
[29] Liu S L, Friel D D 2008 J. Physiol.-London 586 4501
[30] Yang J J, Miao F, Pickett M D, Ohlberg D A A, Stewart D R, Lau C N, Williams R S 2009 Nanotechnology 20 215201
[31] Liu D Q, Cheng H F, Zhu X, Wang G, Wang N N 2013 ACS Appl. Mater. Interfaces 5 11258
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