基于二维材料MXene的仿神经突触忆阻器的制备和长/短时程突触可塑性的实现
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摘要
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/SiO_2/W的仿神经突触忆阻器.结果表明, Cu/MXene/SiO_2/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/SiO_2/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/SiO_2/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/SiO_2/W memristor.By using the unique properties of MXene, the conductance of the memristor can be modulated by the accumulation or reflux of Cu~(2+) at the physical switching layer, which can vividly simulate the mechanism of bio-synapses. Experimental results show that the Cu/MXene/SiO_2/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/SiO_2/W memristor with both long-term and shortterm plasticity will have great application prospects for brain-inspired intelligence in the future.
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/SiO_2/W memristor.By using the unique properties of MXene, the conductance of the memristor can be modulated by the accumulation or reflux of Cu~(2+) at the physical switching layer, which can vividly simulate the mechanism of bio-synapses. Experimental results show that the Cu/MXene/SiO_2/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/SiO_2/W memristor with both long-term and shortterm plasticity will have great application prospects for brain-inspired intelligence in the future.
引文
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[5] Dmitri B S, Gregory S S, Duncan R S, Williams R S 2008Nature 453 80
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[7] Waser R, Dittmann R, Staikov G, Kristof S 2009 Adv. Mater.21 2632
[8] Yu Z Q, Liu M L, Lang J X, Qian K, Zhang C H 2018 Acta Phys.Sin.67 157302(in Chinese)[余志强,刘敏丽,郎建勋,钱楷,张昌华2018物理学报67 157302]
[9] Chang T, Jo S H, Lu W 2011 ACS Nano 5 7669
[10] Kim M K, Lee J S 2018 ACS Nano 12 1680
[11] Hirano T 2018 Cerebellum 17 756
[12] Wang C H, He W, Tong Y, Zhao R 2016 Sci. Rep. 6 22970
[13] Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C, Zhu X J 2012Adv. Funct. Mater. 22 2759
[14] Zhang X M, Liu S, Zhao X L, Wu F C, Wu Q T, Wang W,Cao R R, Fang Y L, Lv H B, Long S B, Liu Q, Liu M 2017IEEE Electron Dev. Lett. 38 1208
[15] Wang F, Ke S 1, Qin C Z, Wang B, Long H, Wang K, Lu P X 2018 Opt. Laser Technol. 103 272
[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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[18] 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. 66217304(in Chinese)[吴全潭,时拓,赵晓龙,张续猛,伍法才,曹荣荣,龙世兵,吕杭炳,刘琦,刘明2017物理学报66 217304]
[19] Liu C Y, Zhang Y X, Yang C P 2017 Sensors Mater. 30 463
[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
[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
[25] Zhao X L, Liu S, Niu J B, Liao L, Liu Q, Xiao X H, Lv H B,Long S B, Banerjee W, Li W Q, Si S Y, Liu M 2017 Small 131603948
[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
[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
[32] Liu Q, Zhang X M, Luo Q, Zhao X L, Lv H B, Long S B, LiuM 2018 Sci. China:Phys. Mech. Astron. 61 088711
[33] Suk J W, Kitt A, Magnuson C W, Hao Y, Ahmed S, An J,Swan A K, Boldberg B B, Ruoff R S 2011 ACS Nano 5 6916