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Néel Tensor Torque in Polycrystalline Antiferromagnets.

Chao-Yao Yang1,2,3, Sheng-Huai Chen1, Chih-Hsiang Tseng1

  • 1Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan.

Advanced Materials (Deerfield Beach, Fla.)
|August 29, 2025
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Summary

Researchers developed a Néel tensor to control polycrystalline antiferromagnets (AFMs) for spintronics. This new method enables field-free spin-orbit torque switching and memory retention in AFM spintronic devices.

Keywords:
field‐free switchingneel tensor torquephysically unclonable functionalityspin‐orbit torque

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Area of Science:

  • Spintronics
  • Materials Science
  • Machine Learning

Background:

  • Antiferromagnets (AFMs) are promising for spintronics due to fast dynamics and stability.
  • Electrical control of polycrystalline AFMs is challenging due to their complex spin structures.
  • Conventional methods rely on the Néel vector, which is insufficient for polycrystalline AFMs.

Purpose of the Study:

  • To introduce a new statistical descriptor, the Néel tensor, for polycrystalline AFMs.
  • To enable field-free spin-orbit torque (SOT) switching in heavy-metal/FM/AFM trilayers.
  • To demonstrate the trainability and memory capabilities of the Néel tensor in AFM spintronics.

Main Methods:

  • Introduction of the Néel tensor, a rank-two symmetric tensor, to statistically describe spin correlations in polycrystalline AFMs.
  • Application of machine learning techniques to extract hidden statistical patterns in AFM spin structures.
  • Experimental demonstration of Néel tensor torque for field-free SOT switching.

Main Results:

  • The Néel tensor effectively captures spin correlations in polycrystalline AFMs, overcoming limitations of the Néel vector.
  • An emergent Néel tensor torque mechanism at the FM/AFM interface enables field-free SOT switching.
  • Experimental evidence shows the Néel tensor can be trained and memorized, enabling polarity retention in AFM spintronics.

Conclusions:

  • The Néel tensor provides a new degree of freedom for controlling polycrystalline AFMs.
  • This work bridges the gap between theoretical models and practical applications of AFMs in spintronics.
  • Findings pave the way for non-volatile, reconfigurable spintronic memory and neuromorphic computing.