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Current‐Controllable and Reversible Multi‐Resistance‐State Based on Domain Wall Number Transition in 2D Ferromagnet Fe<sub>3</sub>GeTe<sub>2</sub>

Chendi Yang, Yalei Huang, Ke Pei, Xiumin Long, Liting Yang, Yongming Luo, Yuxiang Lai, Jincang Zhang, Guixin Cao, Renchao Che

2024Advanced Materials13 citationsDOIOpen Access PDF

Abstract

Abstract Controlling the multi‐state switching is significantly essential for the extensive utilization of 2D ferromagnet in magnetic racetrack memories, topological devices, and neuromorphic computing devices. The development of all‐electric functional nanodevices with multi‐state switching and a rapid reset remains challenging. Herein, to imitate the potentiation and depression process of biological synapses, a full‐current strategy is unprecedently established by the controllable resistance‐state switching originating from the spin configuration rearrangement by domain wall number modulation in Fe 3 GeTe 2 . In particular, a strong correlation is uncovered in the reduction of domain wall number with the corresponding resistance decreasing by in‐situ Lorentz transmission electron microscopy. Interestingly, the magnetic state is reversed instantly to the multi‐domain wall state under a single pulse current with a higher amplitude, attributed to the rapid thermal demagnetization by simulation. Based on the neuromorphic computing system with full‐current‐driven artificial Fe 3 GeTe 2 synapses with multi‐state switching, a high accuracy of ≈91% is achieved in the handwriting image recognition pattern. The results identify 2D ferromagnet as an intriguing candidate for future advanced neuromorphic spintronics.

Topics & Concepts

Neuromorphic engineeringMaterials scienceFerromagnetismSpintronicsCondensed matter physicsDomain wall (magnetism)MemristorBistabilityState (computer science)OptoelectronicsNanotechnologyElectronic engineeringComputer scienceArtificial neural networkPhysicsMagnetizationArtificial intelligenceMagnetic fieldEngineeringAlgorithmQuantum mechanicsAdvanced Memory and Neural Computing2D Materials and ApplicationsFerroelectric and Negative Capacitance Devices