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

doi:10.1088/1674-1056/abccb8

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 TaO_x memristors embedded with silver nanoparticles (Ag NPs) have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs. The TaO_x 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 TaO_x 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: Yunfeng.lai@fzu.edu.cn

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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 TaO_x memristors embedded with silver nanoparticles 2021 Chin. Phys. B 30 047301

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

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