Controllable digital and analog resistive switching behavior of 2D layered WSe2 nanosheets for neuromorphic computing

被引:19
|
作者
Cheng, Siqi [1 ]
Zhong, Lun [1 ]
Yin, Jinxiang [1 ]
Duan, Huan [1 ]
Xie, Qin [2 ]
Luo, Wenbo [2 ]
Jie, Wenjing [1 ]
机构
[1] Sichuan Normal Univ, Coll Chem & Mat Sci, Chengdu 610066, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, State Key Lab Elect Thin Films & Integrated Device, Chengdu 610054, Peoples R China
基金
中国国家自然科学基金;
关键词
GROWTH;
D O I
10.1039/d2nr06580k
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe2 nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 x 10(4). On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe2 nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.
引用
收藏
页码:4801 / 4808
页数:8
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