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

TLDR

Memristors with nonvolatile memory characteristics promise a new era for neuromorphic computing and digital logic, yet devices based on oxygen‑vacancy or metal‑ion filaments typically require large operating currents that hinder low‑power operation. This work aims to develop a low‑power memristor by employing a material whose switching mechanism differs from conventional vacancy or filament pathways. The device’s resistance switching is attributed to the creation of sulfur and tungsten vacancies and electron hopping between these defects. 2D WS₂ memristors in the 2H phase exhibit fast 13‑/14‑ns ON/OFF switching, a 1‑µA program current, femtojoule SET/RESET energy, and can emulate basic synaptic functions, with density‑functional theory confirming that deep defect states from the vacancies suppress charge leakage and enable low‑power neuromorphic operation.

Abstract

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.