Room‐Temperature Skyrmionic Synapse in 2D Ferromagnet Fe <sub>3</sub> GaTe <sub>2</sub> Operating via Collective Spin Texture Transformation

J Jixiang Huang (School of Microelectronics University of Science and Technology of China Hefei Anhui China) T Tongji Zhu (School of Microelectronics University of Science and Technology of China Hefei Anhui China) P PeiYu Cai X Xiaoming Ma Z Zhen Wang R Ruifu Zhang Y Yi Hao J Jingdi Lu L Liubing He (School of Microelectronics University of Science and Technology of China Hefei Anhui China) J Jun Xu Y Yuejie Zhang W Wanjun Jiang J Jing Tao S Shiming Lei P Pavel Parchinskiy (Department of Semiconductor and Polymer Physics, National University of Uzbekistan 6 , Tashkent 100174,) J Jing Teng L Lingfei Wang W Wei Niu E Elton J.G. Santos (Institute for Condensed Matter and Complex Systems School of Physics and Astronomy The University of Edinburgh Edinburgh UK) X Xiaoqian Zhang (School of Physics) P Peng Li

Abstract

ABSTRACT Magnetic skyrmions, as topologically protected spin textures, hold great potential for energy‐efficient neuromorphic systems. While artificial synapses have been demonstrated in magnetic multilayers by electrically controlling skyrmion populations, their probabilistic nucleation severely limits reliability. The recent emergence of 2D van der Waals magnets, with their inherent tunability and novel spintronic phenomena, offers a promising platform to overcome these challenges. Here, we demonstrate an artificial synaptic device in 2D ferromagnet Fe 3 GaTe 2 , operating on the fundamentally different principle of a deterministic and collective spin texture transformation from a skyrmion‐lattice to a stripe‐domain state. This transformation yields a linear, reproducible modulation of the anomalous Hall resistance. The slope of this linear response, defined as the synaptic weight, is effectively tuned by varying the pulse width, thereby enabling multi‐weight functionality and multiply‐accumulate operations. Projected scaling of the device reduces the single‐operation energy consumption to 0.66 pJ, a level comparable to state‐of‐the‐art memristor technologies (e.g., resistive random‐access memory and phase‐change memory). Furthermore, a hardware‐informed quantized neural network based on this synapse achieves a high recognition accuracy (∼96.1%) in handwritten‐digit recognition. Our findings establish a robust pathway for creating large‐scale and energy‐efficient neuromorphic systems based on the collective dynamics of Fe 3 GaTe 2 spin textures at room temperature.

Article Details

Volume / Issue Vol. 1, Issue 1
Published May 20, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (21)

J

Jixiang Huang

School of Microelectronics University of Science and Technology of China Hefei Anhui China

T

Tongji Zhu

School of Microelectronics University of Science and Technology of China Hefei Anhui China

P

PeiYu Cai

X

Xiaoming Ma

Z

Zhen Wang

R

Ruifu Zhang

Y

Yi Hao

J

Jingdi Lu

L

Liubing He

School of Microelectronics University of Science and Technology of China Hefei Anhui China

J

Jun Xu

Y

Yuejie Zhang

W

Wanjun Jiang

J

Jing Tao

S

Shiming Lei

P

Pavel Parchinskiy

Department of Semiconductor and Polymer Physics, National University of Uzbekistan 6 , Tashkent 100174,

J

Jing Teng

L

Lingfei Wang

W

Wei Niu

E

Elton J.G. Santos

Institute for Condensed Matter and Complex Systems School of Physics and Astronomy The University of Edinburgh Edinburgh UK

X

Xiaoqian Zhang

School of Physics

P

Peng Li