4.8 Article

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

Journal

SMALL
Volume 15, Issue 24, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.201901423

Keywords

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

Funding

  1. National Natural Science Foundation of China [61674050, 61874158, 61825404, 61732020]
  2. Top-Notch Youth Project in Hebei Province [BJ2014008]
  3. Outstanding Youth Project of Hebei Province [F2016201220]
  4. Outstanding Youth Cultivation Project of Hebei University [2015JQY01]
  5. Project of Science and Technology Activities for Overseas Researcher [CL201602]
  6. Institute of Baoding Nanyang ResearchNew Material Technology Platform [17H03]
  7. Project of Distinguished Young of Hebei Province [A2018201231]
  8. Training Program of Innovation and Entrepreneurship for Undergraduates [201710075013, 2017075]
  9. Support Program for the Top Young Talents of Hebei Province [70280011807]
  10. Training and Introduction of High-Level Innovative Talents of Hebei University [801260201300]
  11. Hundred Persons Plan of Hebei Province [606999919001]

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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 mu A 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.

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