Néel Tensor Torque in Polycrystalline Antiferromagnets

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.