Tunneling current-controlled spin states in few-layer van der Waals magnets

ZhuangEn Fu, Piumi I. Samarawickrama, John Ackerman, Yanglin Zhu, Zhiqiang Mao, Kenji Watanabe, Takashi Taniguchi, Wenyong Wang, Yuri Dahnovsky, Mingzhong Wu, TeYu Chien, Jinke Tang, Allan H. MacDonald, Hua Chen () and Jifa Tian ()
Additional contact information
ZhuangEn Fu: University of Wyoming
Piumi I. Samarawickrama: University of Wyoming
John Ackerman: University of Wyoming
Yanglin Zhu: The Pennsylvania State University
Zhiqiang Mao: The Pennsylvania State University
Kenji Watanabe: National Institute for Materials Science
Takashi Taniguchi: National Institute for Materials Science
Wenyong Wang: University of Wyoming
Yuri Dahnovsky: University of Wyoming
Mingzhong Wu: Northeastern University
TeYu Chien: University of Wyoming
Jinke Tang: University of Wyoming
Allan H. MacDonald: The University of Texas at Austin
Hua Chen: Colorado State University
Jifa Tian: University of Wyoming

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI3. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI3, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI3 layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI3 tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing.

Date: 2024
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DOI: 10.1038/s41467-024-47820-5

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