Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-Based Neuromorphic Memristor

被引：72

作者：

Liu, Bo ^{[1
,2
]}

Liu, Zhiwei ^{[1
]}

Chiu, In-Shiang ^{[2
]}

Di, MengFu ^{[4
]}

Wu, YongRen ^{[5
]}

Wang, Jer-Chyi ^{[2
,6
,8
]}

Hou, Tuo-Hung ^{[10
,11
]}

Lai, Chao-Sung ^{[2
,3
,7
,9
]}

机构：

[1] Univ Elect Sci & Technol China, State Key Lab Elect Thin Films & Integrate Device, Chengdu 610054, Peoples R China

[2] Chang Gung Univ, Dept Elect Engn, Biomed Engn Res Ctr, Taoyuan 33302, Taiwan

[3] Chang Gung Univ, Biosensor Grp, Biomed Engn Res Ctr, Taoyuan 33302, Taiwan

[4] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA

[5] Integrated Serv Technol, Shanghai, Peoples R China

[6] Chang Gung Mem Hosp, Dept Neurosurg, Taoyuan 33305, Taiwan

[7] Chang Gung Mem Hosp, Dept Nephrol, Taoyuan 33305, Taiwan

[8] Ming Chi Univ Technol, Dept Elect Engn, New Taipei 24301, Taiwan

[9] Ming Chi Univ Technol, Dept Mat Engn, New Taipei 24301, Taiwan

[10] Natl Chiao Tung Univ, Dept Elect Engn, Hsinchu 300, Taiwan

[11] Natl Chiao Tung Univ, Inst Elect, Hsinchu 300, Taiwan

来源：

ACS APPLIED MATERIALS & INTERFACES | 2018年 / 10卷 / 24期

关键词：

graphene electrode; neuromorphic memristor; artificial synapses; below femtojoule spiking energy; programmable metaplasticity; spike-timing dependent plasticity; NEURAL-NETWORKS; PHYSICAL MODEL; PLASTICITY; DEVICE; MEMORY;

D O I：

10.1021/acsami.8b04685

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Memristors with rich interior dynamics of ion migration are promising for mimicking various biological synaptic functions in neuromorphic hardware systems. A graphene-based memristor shows an extremely low energy consumption of less than a femtojoule per spike, by taking advantage of weak surface van der Waals interaction of graphene. The device also shows an intriguing programmable metaplasticity property in which the synaptic plasticity depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus. This graphene-based memristor could be a promising building block toward designing highly versatile and extremely energy efficient neuromorphic computing systems.

引用

页码：20237 / 20243

页数：7

共 6 条

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