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Small. 2019 Jun;15(24):e1901423. doi: 10.1002/smll.201901423. Epub 2019 May 2.

Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet-Based Memristor for Low-Power Neuromorphic Computing.

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National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China.
Department of Materials Science and Engineering, National University of Singapore, Singapore, 117576, Singapore.
Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, P. R. China.


Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal-ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low-power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal-ion conductive filaments to realize low-power operation. Herein, high-performance and low-power consumption memristors based on 2D WS2 with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low-power consumption for neuromorphic computing application.


2D materials; WS2 nanosheets; density functional theory calculations; low-power; memristors; vacancies


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