Largely tunable compensation temperature in a rare-earth ferrimagnetic metal and deterministic spin-orbit torque switching for artificial neural network application

  • Li Liu
  • , Yuzhou He
  • , Yifei Ma
  • , Peixin Qin*
  • , Hongyu Chen
  • , Xiaoning Wang
  • , Xiaorong Zhou
  • , Ziang Meng
  • , Guojian Zhao
  • , Zhiyuan Duan
  • , Dazhuang Kang
  • , Yu Liu
  • , Shuai Ning
  • , Zhaochu Luo
  • , Qinghua Zhang
  • , Zhiqi Liu
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications. Magnetization compensation temperature (TM) is a fundamentally critical magnetic parameter for ferrimagnets besides their Curie temperature. Around TM, the spin-orbit switching efficiencies are extraordinarily high. Therefore, the accurate manipulation of TM from the material fabrication process is essential for the electrical steering of ferrimagnetic spins. In this work, CoTb thin films, with the 3d and 4f magnetic sublattices antiferromagnetically coupled to each other, are deposited at different temperatures. The magnetotransport and magnetic properties of these films are systematically investigated. It was found that the TM of this rare-earth ferrimagnet largely depends on the growth temperature and it can be tuned by over 100 K. Accordingly, the spins of an optimized ferrimagnetic CoTb thin film with its TM close to room temperature can be efficiently switched by the current-pulse-induced spin-orbit torque. Moreover, an artificial neural network utilizing the spin-orbit torque device was constructed, demonstrating an image recognition accuracy of approximately 92.5 %, which is comparable to that of conventional software solutions. Thus, this work demonstrates the large tunability of TM of a rare earth ferrimagnet by chemical ordering and the great potential of such a ferrimagnet for electrically operated spintronic devices.

Original languageEnglish
Pages (from-to)15-23
Number of pages9
JournalJournal of Materials Science and Technology
Volume234
DOIs
StatePublished - 1 Nov 2025

Keywords

  • Artificial neural network
  • CoTb
  • Compensation temperatures
  • Ferrimagnetic metals
  • Spin-orbit torque

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