Evidence map›Paper›PMID 41972659›Full record

ArticleNanomaterials (Basel, Switzerland)2026

Nonvolatile Reconfigurable Synthetic Antiferromagnetic Devices Induced by Spin-Orbit Torque for Multifunctional In-Memory Computing.

Mingxu Song, Jiahao Liu, Zhihong Zhu

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mingxu SongCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Jiahao LiuCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0002-0173-4116
Zhihong ZhuCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.

Funding

National Natural Science Foundation of China Grant Nos.12304160National Natural Science Foundation of China Grant Nos. 62274183National Natural Science Foundation of China Grant Nos. 62301595National University of Defense Technology Grant Nos. 23-ZZCX-ZZGC-01-02National University of Defense Technology Grant Nos. ZK24-18
6 · The paper itself

Abstract

The proliferation of intelligent edge devices demands compact, low-power hardware capable of dynamically switching between sensing, logic, and learning tasks-a versatility that traditional multi-chip solutions fundamentally lack. Here, we demonstrate a reconfigurable spin-orbit torque (SOT) device based on an FeTb/Ru/Co synthetic antiferromagnetic (SAF) heterostructure. By modulating the input current amplitude, the device dynamically switches between two distinct operating modes: saturation and activation. In the saturation regime (>80 mA), deterministic magnetization reversal enables Boolean logic operations (AND, NOR). In the activation regime (<80 mA), gradual, non-volatile conductance modulation emulates synaptic plasticity. Benefiting from the strong antiferromagnetic coupling and near-zero net magnetization of the SAF structure, all operations are achieved without external magnetic fields. This single-device, dual-mode reconfigurable architecture establishes a new paradigm for high-density, low-power, multifunctional in-memory computing units, with promise for advancing adaptive edge computing chips.

Indexed as

artificial neuronartificial synapselogic-in-memoryspin orbit torquesynthetic antiferromagnet

Identifiers

PMID41972659
PMCPMC13074596

What OpenQuestion holds

Textmetadata
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.