Decoupling Magnetic and Electric Field Control in Magneto-Ionic Materials for Energy-Efficient Brain-Inspired Memory Devices

Magneto-ionic materials, which enable nonvolatile control of magnetism through voltage-driven ion migration, are emerging as promising candidates for neuromorphic computing. Unlike conventional memristors, these systems allow dual actuation by both electric and magnetic fields, providing a broader range of functional capabilities. The reliance on voltage rather than current significantly reduces Joule heating and enhances the energy efficiency. However, the general need for external magnetic fields to modulate the voltage-induced magnetic response remains a key limitation, undermining the full energy-saving potential of these systems. In this work, we present a magneto-ionic strategy in CoFeN that fully decouples the electric and magnetic field requirements. By taking advantage of a planar N<sup>3-</sup> ion migration and the ferromagnetic exchange interactions between preexisting and newly generated CoFe magnetic regions, we achieve remanent-state magnetization control solely through applied voltage. The system exhibits behaviors reminiscent of neuromorphic-inspired functionalities, such as synaptic potentiation and depression, while also exhibiting a cumulative voltage-driven increase in magnetization in the absence of a magnetic field. Once the magnetic field is switched off, synaptic weight remains influenced by both the sample's magnetic and electric history. By eliminating the need for magnetic fields, our approach contributes to reduce energy consumption, offering a more efficient pathway for brain-inspired magneto-ionic devices.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-12-24
DOI
https://doi.org/10.1021/acsami.5c19791
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Decoupling Magnetic and Electric Field Control in Magneto-Ionic Materials for Energy-Efficient Brain-Inspired Memory Devices

Huan Tan, Irena Spasojević, Luis Martínez Armesto, Eva Pellicer et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Decoupling Magnetic and Electric Field Control in Magneto-Ionic Materials for Energy-Efficient Brain-Inspired Memory Devices

Huan Tan, Irena Spasojević, Luis Martínez Armesto, Eva Pellicer, Jordi Sort, Zheng Ma
article en

Abstract

Magneto-ionic materials, which enable nonvolatile control of magnetism through voltage-driven ion migration, are emerging as promising candidates for neuromorphic computing. Unlike conventional memristors, these systems allow dual actuation by both electric and magnetic fields, providing a broader range of functional capabilities. The reliance on voltage rather than current significantly reduces Joule heating and enhances the energy efficiency. However, the general need for external magnetic fields to modulate the voltage-induced magnetic response remains a key limitation, undermining the full energy-saving potential of these systems. In this work, we present a magneto-ionic strategy in CoFeN that fully decouples the electric and magnetic field requirements. By taking advantage of a planar N<sup>3-</sup> ion migration and the ferromagnetic exchange interactions between preexisting and newly generated CoFe magnetic regions, we achieve remanent-state magnetization control solely through applied voltage. The system exhibits behaviors reminiscent of neuromorphic-inspired functionalities, such as synaptic potentiation and depression, while also exhibiting a cumulative voltage-driven increase in magnetization in the absence of a magnetic field. Once the magnetic field is switched off, synaptic weight remains influenced by both the sample's magnetic and electric history. By eliminating the need for magnetic fields, our approach contributes to reduce energy consumption, offering a more efficient pathway for brain-inspired magneto-ionic devices.

ACS Applied Materials & InterfacesVol. 18(1)
Institució Catalana de Recerca i Estudis Avançats (ES), Universitat Autònoma de Barcelona (ES), Institut Català de Nanociència i Nanotecnologia (ES)
Affordable and clean energy
Openalex Percentile: Top 42%
Advanced Memory and Neural Computing
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