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December 10, 2025Journal of the American Chemical Society3 citations

Electric-Field-Controlled Altermagnetic Transition for Neuromorphic Computing

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ZDZhiyuan DuanKementerian Pendidikan MalaysiaPQPeixin QinKementerian Pendidikan Malaysia
Chongke Zhong
Chongke ZhongGeneral / Preventive / Lipids

Key Points

  • The research aims to investigate electric-field control of altermagnetism for neuromorphic computing applications.
  • Utilized strain-mediated coupling in MnTe/PMN-PT heterostructures.
  • Measured the modulation of Néel temperature with applied electric fields.
  • Assessed resistance modulation around the magnetic phase transition temperature.
  • Achieved modulation of Néel temperature from 310 to 328 K.
  • Induced up to 9.7% resistance modulation around the magnetic phase transition temperature.
  • Demonstrated 100% pattern recognition accuracy in a Hopfield neuromorphic network at ≤40% noise levels.

Abstract

Altermagnets represent a novel magnetic phase with transformative potential for ultrafast spintronics, yet efficient control of their magnetic states remains challenging. We demonstrate an ultralow-power electric-field control of altermagnetism in MnTe through strain-mediated coupling in MnTe/PMN-PT heterostructures with negligible Joule heating. Application of +6 kV/cm electric fields induces piezoelectric strain in PMN-PT, modulating the Néel temperature from 310 to 328 K. As a result, around the magnetic phase transition, the altermagnetic spin splitting of MnTe is reversibly switched "on" and "off" by the electric fields. Meanwhile, the piezoelectric strain generates lattice distortions and magnetic structure changes in MnTe, enabling up to a 9.7% resistance modulation around the magnetic phase transition temperature. Leveraging this effect, we implement programmable resistance states in a Hopfield neuromorphic network, achieving 100% pattern recognition accuracy at ≤40% noise levels. This approach establishes electric-field control as a low-power strategy for altermagnetic manipulation while demonstrating the viability of altermagnetic materials for energy-efficient neuromorphic computing beyond conventional charge-based architectures.

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Cite This Study

Duan et al. (2025) studied this question.

synapsesocial.com/papers/69401d472d562116f28f876bhttps://doi.org/10.1021/jacs.5c15276
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