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Low-Power Memristor Based on Two-Dimensional Materials

Huan Duan, Siqi Cheng, Ling Qin, Xuelian Zhang, Bingyang Xie, Yang Zhang, Wenjing Jie

2022The Journal of Physical Chemistry Letters122 citationsDOI

Abstract

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.

Topics & Concepts

Neuromorphic engineeringMemristorResistive random-access memoryMaterials scienceReset (finance)NanotechnologyComputer scienceHexagonal boron nitrideElectronic engineeringPower (physics)Engineering physicsVoltageElectrical engineeringArtificial neural networkEngineeringArtificial intelligencePhysicsEconomicsGrapheneQuantum mechanicsFinancial economicsAdvanced Memory and Neural ComputingMXene and MAX Phase MaterialsFerroelectric and Negative Capacitance Devices
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