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Nanoscale exchange-bias magnetic tunnel junctions enabled memristive synapse and leaky-integrate-fire neuron for neuromorphic computing.

A correction to this paper has been published: the notice, 42806029, from Europe PMC.

Overview

Authors: Zanhong Chen1, Dehang Zhu1, Ao Du1, Yuzhang Shi1, Wenlong Cai1,2, Zixi Wang1, Yuqi Duan1, Shiyang Lu1, Kaihua Cao1, He Zhang1, Deming Zhang1, Hongxi Liu3, Kewen Shi1,2, Weisheng Zhao1,2
ORCID iDs: Ao Du, Wenlong Cai, Zixi Wang, Hongxi Liu, Kewen Shi, Weisheng Zhao
  1. Fert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University,Beijing, China
  2. State Key Laboratory of Spintronics, Hangzhou International Innovation Institute, Beihang University,Hangzhou, China
  3. Truth Memory Tech. Corporation, Beijing, China
Institutions: Beihang University (China)
Journal: Nature communications, volume 17, issue 1, article 4362
Dates: received 9 September 2025; accepted 28 February 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70802-8 · PMID 41876517 · PMCID PMC13172308 · OpenAlex W7140178330
Open access: gold, a free copy (OpenAlex)
Status: code on request (how the code is found and verified)
Categories: cellular / molecular (subfield)
Methods: Machine learning
Keywords: Electronic devices, Magnetic devices
MeSH: Nanotechnology*, Neural Networks, Computer*, Neurons*, Synapses*, Action Potentials, Models, Neurological, Soft Computing (* major topic)
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: 国家重点研发计划（资助号2022YFB4400200,2024YFA1408802） 中国国家自然科学基金（拨款编号T2394473，T2394474,62271026,62401026和623B2015） 北京优秀青年科学家计划和中国浙江省自然科学基金会（LZ24F040003）
Citations: cited by 2 papers (Europe PMC); 52 references in the paper
Notices: A correction to this paper has been published (42806029, from Europe PMC)

Abstract

Neuromorphic computing implemented by spintronic memories offers computational advantages, including in-memory computing capability, high energy efficiency, and near-unlimited endurance. However, their basic units, magnetic tunnel junctions (MTJs), face inherent challenges in emulating analog synapses and spiking neurons due to their bistable resistance states and lack of bio-realistic switching dynamics. Here, we experimentally demonstrate on-chip co-integrated memristive synapse and leaky-integrate-fire (LIF) neuron designed with nanoscale exchange-bias MTJs (EB-MTJs). By exploiting the spatial distribution of antiferromagnets and its current-dependent modulation, we achieve stable continuous multi-state synaptic behavior with spike-timing-dependent-plasticity (STDP) characteristics in compact (~100 nm) EB-MTJs. Furthermore, time-resolved measurements reveal that EB-MTJs can be progressively programmed by 0.4 ns pulses, emulating LIF neuronal dynamics with high operational bandwidth in the gigahertz range. Finally, we construct a convolutional spiking neural network based on EB-MTJs and achieve 96% accuracy in gesture recognition via a hybrid backpropagation-STDP algorithm, highlighting their potential in neuromorphic computing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

The codes used in this paper are available from the corresponding author upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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Data

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Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 7 MeSH terms, 1 funder, 47 references, 1 integrity notice.

Cite

This paper

Chen, Z., Zhu, D., Du, A., Shi, Y., Cai, W., Wang, Z., Duan, Y., Lu, S., Cao, K., Zhang, H., Zhang, D., Liu, H., Shi, K., & Zhao, W. (2026). Nanoscale exchange-bias magnetic tunnel junctions enabled memristive synapse and leaky-integrate-fire neuron for neuromorphic computing. Nature communications, 17(1), 4362. https://doi.org/10.1038/s41467-026-70802-8

BibTeX

@article{chen2026nanoscale,
author = {Chen, Zanhong and Zhu, Dehang and Du, Ao and Shi, Yuzhang and Cai, Wenlong and Wang, Zixi and Duan, Yuqi and Lu, Shiyang and Cao, Kaihua and Zhang, He and Zhang, Deming and Liu, Hongxi and Shi, Kewen and Zhao, Weisheng},
title = {{Nanoscale exchange-bias magnetic tunnel junctions enabled memristive synapse and leaky-integrate-fire neuron for neuromorphic computing}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {4362},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70802-8},
url = {https://doi.org/10.1038/s41467-026-70802-8},
pmid = {41876517},
pmcid = {PMC13172308}
}

RIS

TY - JOUR
AU - Chen, Zanhong
AU - Zhu, Dehang
AU - Du, Ao
AU - Shi, Yuzhang
AU - Cai, Wenlong
AU - Wang, Zixi
AU - Duan, Yuqi
AU - Lu, Shiyang
AU - Cao, Kaihua
AU - Zhang, He
AU - Zhang, Deming
AU - Liu, Hongxi
AU - Shi, Kewen
AU - Zhao, Weisheng
TI - Nanoscale exchange-bias magnetic tunnel junctions enabled memristive synapse and leaky-integrate-fire neuron for neuromorphic computing
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/24
VL - 17
IS - 1
SP - 4362
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70802-8
UR - https://doi.org/10.1038/s41467-026-70802-8
LA - en
ER -

CSL-JSON

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