Evidence map›Paper›PMID 41876517›Full record

ArticleNature communications2026

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

Zanhong Chen, Dehang Zhu, Ao Du, Yuzhang Shi, Wenlong Cai, Zixi Wang, Yuqi Duan, Shiyang Lu, Kaihua Cao, He Zhang and 4 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Zanhong Chen *Fert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Dehang Zhu *Fert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Ao Du *Fert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Yuzhang Shi *Fert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Wenlong CaiFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China. caiwenlong1993@buaa.edu.cn.ORCID http://orcid.org/0000-0001-9240-8086
Zixi WangFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.ORCID http://orcid.org/0009-0007-8258-4298
Yuqi DuanFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Shiyang LuFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Kaihua CaoFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
He ZhangFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Deming ZhangFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China.
Hongxi LiuTruth Memory Tech. Corporation, Beijing, China.ORCID http://orcid.org/0009-0004-1562-2539
Kewen ShiFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China. shikewen@buaa.edu.cn.ORCID http://orcid.org/0000-0002-1294-3220
Weisheng ZhaoFert Beijing Institute, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China. weisheng.zhao@buaa.edu.cn.ORCID http://orcid.org/0000-0001-8088-0404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

NanotechnologyNeural Networks, ComputerNeuronsSynapsesAction PotentialsModels, NeurologicalSoft Computing

Identifiers

PMID41876517
PMCPMC13172308

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.