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Chin. Phys. B, 2021, Vol. 30(4): 047301    DOI: 10.1088/1674-1056/abccb8

Implementation of synaptic learning rules by TaOx memristors embedded with silver nanoparticles

Yue Ning(宁玥), Yunfeng Lai(赖云锋), Jiandong Wan(万建栋), Shuying Cheng(程树英), Qiao Zheng(郑巧), and Jinling Yu(俞金玲)
1 School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
Abstract  As an alternative device for neuromorphic computing to conquer von Neumann bottleneck, the memristor serving as an artificial synapse has attracted much attention. The TaOx memristors embedded with silver nanoparticles (Ag NPs) have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs. The TaOx memristors with and without Ag NPs are capable of simulating synaptic plasticity (PTP, STDP, and STP to LTP), learning, and memory behaviors. The conduction of the high resistance state (HRS) is driven by Schottky-emission mechanism. The embedment of Ag NPs causes the low resistance state (LRS) conduction governed by a Poole-Frenkel emission mechanism instead of a space-charge-limited conduction (SCLC) in a pure TaOx system, which is ascribed to the Ag NPs enhancing electric field to produce additional traps and to reduce Coulomb potential energy of bound electrons to assist electron transport. Consequently, the enhanced electric fields induced by Ag NPs increase the learning strength and learning speed of the synapses. Additionally, they also improve synaptic sensitivity to stimuli. The linearity of conductance modulation and the reproducibility of conductance are improved as well.
Keywords:  resistive switching      synaptic plasticity      memristor  
Received:  28 September 2020      Revised:  12 November 2020      Accepted manuscript online:  23 November 2020
PACS:  73.40.Rw (Metal-insulator-metal structures)  
  77.80.Fm (Switching phenomena)  
  87.19.lg (Synapses: chemical and electrical (gap junctions))  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61674038), the Natural Science Foundation of Fujian Province, China (Grant No. 2019J01218), the Fund from the Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China (Grant No. 2021ZR145), and the Science Fund from the Fujian Provincial Department of Industry and Information Technology of China (Grant No. 82318075).
Corresponding Authors:  Corresponding author. E-mail:   

Cite this article: 

Yue Ning(宁玥), Yunfeng Lai(赖云锋), Jiandong Wan(万建栋), Shuying Cheng(程树英), Qiao Zheng(郑巧), and Jinling Yu(俞金玲) Implementation of synaptic learning rules by TaOx memristors embedded with silver nanoparticles 2021 Chin. Phys. B 30 047301

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