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Highly cyclable voltage control of magnetism in cobalt ferrite nanopillars for memory and neuromorphic applications

Accepted version
Peer-reviewed

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Abstract

Tuning the properties of magnetic materials by voltage-driven ion migration (magneto-ionics) gives potential for energy-efficient, non-volatile magnetic memory and neuromorphic computing. Here, we report large changes in the magnetic moment at saturation (mS) and coercivity (HC), of 34% and 78%, respectively, in an array of CoFe2O4 (CFO) epitaxial nanopillar electrodes (∼50 nm diameter, ∼70 nm pitch, and 90 nm in height) with an applied voltage of −10 V in a liquid electrolyte cell. Furthermore, a magneto-ionic response faster than 3 s and endurance >2000 cycles are demonstrated. The response time is faster than for other magneto-ionic films of similar thickness, and cyclability is around two orders of magnitude higher than for other oxygen magneto-ionic systems. Using a range of characterization techniques, magnetic switching is shown to arise from the modulation of oxygen content in the CFO. Also, the highly cyclable, self-assembled nanopillar structures were demonstrated to emulate various synaptic behaviors, exhibiting non-volatile, multilevel magnetic states for analog computing and high-density storage. Overall, CFO nanopillar arrays offer the potential to be used as interconnected synapses for advanced neuromorphic computing applications.

Description

Journal Title

APL Materials

Conference Name

Journal ISSN

2166-532X
2166-532X

Volume Title

Publisher

AIP Publishing

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Except where otherwised noted, this item's license is described as Attribution 4.0 International
Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (861145)
European Commission Horizon 2020 (H2020) ERC (882929)
This work has received funding from the European Union’s Horizon 2020 research and innovation programme BeMAGIC under the Marie Sklodowska-Curie grant agreement No 861145. JLM-D and TM acknowledge support from the Royal Academy of Engineering Chair in Emerging Technologies Grant CiET1819\24, and the European Research Council ERC Advanced (grant agreement EU-H2020-ERC-ADG #882929), EROS. S.L. and J.L.M.-D. thank Trinity College at Cambridge for partial support. Partial financial support from the Spanish Government (PID2020-116844RB-C21, PDC2021-121276-C31, PID2020 116844RB-C22, and PDC2021-121276-C32), and the Generalitat de Catalunya (2021-SGR 00651) is also acknowledged. A.N acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement Nº 892661– MAGNUS. BZ. We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1).