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

Area of Science:

• Materials Science
• Nanotechnology
• Computer Engineering

Background:

• Memristors are crucial for neuromorphic computing and digital logic due to their nonvolatile memory.
• Existing memristor technologies often suffer from high operating currents, hindering low-power applications.
• Novel materials and mechanisms are needed to overcome the limitations of oxygen vacancy and metal-ion conductive filament memristors.

Purpose of the Study:

• To develop high-performance, low-power consumption memristors.
• To explore two-dimensional tungsten disulfide (2D WS₂) as a memristive material.
• To understand the underlying mechanism responsible for the memristive behavior in 2D WS₂.

Main Methods:

• Fabrication of memristors using 2D WS₂ with 2H phase.
• Characterization of electrical properties, including switching times and operating currents.
• Investigation of the resistance switching mechanism using density functional theory (DFT) calculations.

Main Results:

• Demonstrated memristors with fast switching times (13 ns ON, 14 ns OFF).
• Achieved low program current (1 µA) and femtojoule-level switching energy.
• Successfully mimicked basic biological synaptic functions.
• Identified sulfur and tungsten vacancies and electron hopping as the dominant switching mechanism.

Conclusions:

• 2D WS₂ memristors offer a promising solution for low-power neuromorphic computing.
• The deep-level defect states formed by vacancies effectively prevent charge leakage.
• The proposed mechanism enables efficient and low-power resistance switching for advanced electronic applications.