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Strain-Mediated Magnetization Switching Behavior in a Bicomponent Nanomagnet

Jia-Hui Yuan1, Xiao-Kuo Yang1,*, Bo Wei1, Ya-Bo Chen2, Huan-Qing Cui1, Jia-Hao Liu2, Shu-Qing Dou1, Ming-Xu Song3, and Li Fei4

  • 1Fundamentals Department, Air Force Engineering University, Xi’an 710051, China
  • 2College of Computer, National University of Defense Technology, Changsha 410005, China
  • 3College of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha 410005, China
  • 4Aeronautics Engineering College, Air Force Engineering University, Xi’an 710051, China

  • *yangxk0123@163.com
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Phys. Rev. Applied 19, 014003 – Published 3 January, 2023

DOI: https://doi.org/10.1103/PhysRevApplied.19.014003

Abstract

The strain-mediated method is considered to be a promising solution for implementing energy-efficient magnetization rotation. However, current methods strictly require a voltage clock. Here, a multiferroic nanomagnet composed of bicomponent magnetic materials (Terfenol-D:Ni = 1:2) is developed to study strain-mediated magnetization switching behavior. With micromagnetic simulations, what is demonstrated is that the strict requirements for a precise applied voltage period can be overcome in such a bicomponent nanomagnet, where the threshold of the square-wave voltage pulse width required for complete and repeated magnetization reversal is only 0.42 ns if the amplitude of the voltage is 1 V. Besides deterministic magnetization switching, further study shows that the unique strain-mediated stochastic magnetization reversal behavior of the designed device can be used to mimic the biological neuron. With the application of the derived neural device parameters, a three-layer artificial neural network is further constructed to recognize a handwritten dataset, based on which, an accuracy of more than 98% can be achieved. Overall, these results open an intriguing way toward straintronic memory and neuromorphic systems.

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Phys. Rev. Applied 19, 014003– Published 3 January, 2023
  • Received 2 May 2022
  • Revised 30 August 2022
  • Accepted 15 November 2022
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