Differential magneto-ionic artificial synapse

Magneto-ionics offers exciting opportunities to introduce magnetic functionalities into ionics-based artificial synapses. However, learning applications require symmetry and linearity in the weight-update profile, which remains a challenge not only for magneto-ionics but also for all ionics-based synaptic devices. We demonstrate a magneto-ionic synaptic element exhibiting symmetric potentiation and depression under unipolar gate operation and differential anomalous Hall effect readout. The device is composed of two interconnected Ta/CoFeB/HfO2 Hall bars with individual ionic-gate electrodes able to induce a spin-reorientation transition from perpendicular to in-plane magnetic anisotropy. The combined anomalous Hall effect signal is measured through shared Hall voltage leads, enabling the direct subtraction of the individual signals by applying bias currents of opposite polarity to each Hall bar. This configuration enables synaptic operation using exclusively negative gate voltages; gating one Hall bar induces synaptic potentiation, while gating the other one results in synaptic depression. Operating exclusively under gate voltages of the same polarity, this device architecture avoids the asymmetries in magneto-ionic response, switching speed, and retention that appear with the use of positive and negative gate voltage pulses for potentiation and depression. In addition, operating under an external magnetic field provides dynamic control of the weight-update linearity in both potentiation and depression. The proposed approach provides a robust route toward linear and symmetric synaptic behavior in devices that combine magnetic and ionic functionalities.

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Publication Details

Journal
Applied Physics Letters
Published
2026-06-08
DOI
https://doi.org/10.1063/5.0334517
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Differential magneto-ionic artificial synapse

Himadri Nandan Mohanty, J. Langer, Damien Querlioz, Maria‐Andromachi Syskaki et al.
Applied Physics Letters
Advanced Memory and Neural Computing
article

Differential magneto-ionic artificial synapse

Himadri Nandan Mohanty, J. Langer, Damien Querlioz, Maria‐Andromachi Syskaki, L. Herrera Diez, K. Cottart, G. Bernard, A. Durnez
article en

Abstract

Magneto-ionics offers exciting opportunities to introduce magnetic functionalities into ionics-based artificial synapses. However, learning applications require symmetry and linearity in the weight-update profile, which remains a challenge not only for magneto-ionics but also for all ionics-based synaptic devices. We demonstrate a magneto-ionic synaptic element exhibiting symmetric potentiation and depression under unipolar gate operation and differential anomalous Hall effect readout. The device is composed of two interconnected Ta/CoFeB/HfO2 Hall bars with individual ionic-gate electrodes able to induce a spin-reorientation transition from perpendicular to in-plane magnetic anisotropy. The combined anomalous Hall effect signal is measured through shared Hall voltage leads, enabling the direct subtraction of the individual signals by applying bias currents of opposite polarity to each Hall bar. This configuration enables synaptic operation using exclusively negative gate voltages; gating one Hall bar induces synaptic potentiation, while gating the other one results in synaptic depression. Operating exclusively under gate voltages of the same polarity, this device architecture avoids the asymmetries in magneto-ionic response, switching speed, and retention that appear with the use of positive and negative gate voltage pulses for potentiation and depression. In addition, operating under an external magnetic field provides dynamic control of the weight-update linearity in both potentiation and depression. The proposed approach provides a robust route toward linear and symmetric synaptic behavior in devices that combine magnetic and ionic functionalities.

Applied Physics LettersVol. 128(23)
Centre National de la Recherche Scientifique (FR), Singulus (Germany) (DE), Université Paris-Saclay (FR), Centre de Nanosciences et de Nanotechnologies (FR)
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
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