The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Magnetic skyrmion-based synaptic devices
Open accessAbstract
Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density. A wide variety of skyrmionic device concepts and prototypes have recently been proposed, highlighting their potential applications. Furthermore, the intrinsic properties of skyrmions enable new functionalities that may be inaccessible to conventional electronic devices. Here, we report on a skyrmion-based artificial synapse device for neuromorphic systems. The synaptic weight of the proposed device can be strengthened/weakened by positive/negative stimuli, mimicking the potentiation/depression process of a biological synapse. Both short-term plasticity and long-term potentiation functionalities have been demonstrated with micromagnetic simulations. This proposal suggests new possibilities for synaptic devices in neuromorphic systems with adaptive learning function.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 1×
“tronic devices have been proposed to emulate synapses, including phase-change memories [7, 8], Ag–Si memories [9], and resistive memories [10–12], etc.”
BackgroundTraining and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., & Aono, M. (2011) · Nature Materials · doi:10.1038/nmat3054 · Cited by 1,793
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Kuzum, D., Jeyasingh, R. G. D., Lee, B., & Wong, H.-S. P. (2012) · Nano Letters · doi:10.1021/nl201040y · Cited by 1,267
Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses
Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2013) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2013.2263000 · Cited by 229
Cited by
A comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 758
Perspective: Magnetic skyrmions—Overview of recent progress in an active research field
Everschor-Sitte, K., Masell, J., Reeve, R. M., & Kläui, M. (2018) · Journal of Applied Physics · doi:10.1063/1.5048972 · Cited by 601
Skyrmions in magnetic multilayers
Jiang, W., Chen, G., Liu, K., Zang, J., te Velthuis, S. G. E., & Hoffmann, A. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.08.001 · Cited by 566
Thermal skyrmion diffusion used in a reshuffler device
Zázvorka, J., Jakobs, F., Heinze, D., Keil, N., Kromin, S., Jaiswal, S., Litzius, K., Jakob, G., Virnau, P., Pinna, D., et al. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0436-8 · Cited by 346
Prychynenko, D., Sitte, M., Litzius, K., Krüger, B., Bourianoff, G., Kläui, M., Sinova, J., & Everschor-Sitte, K. (2018) · Physical Review Applied · doi:10.1103/physrevapplied.9.014034 · Cited by 293
Metadata
- Authors
- Huang, Y., Kang, W., Zhang, X., Zhou, Y., & Zhao, W.
- Venue
- Nanotechnology
- Publisher
- IOP Publishing
- Published
- 2017-01-23
- Volume
- 28
- Issue
- 8
- Pages
- 08LT02
- ISSN
- 0957-4484 (ISSN-L)
- 1361-6528
- OpenAlex
- W2511518502
- Cited by
- 300
- References
- 6
- FWCI
- 10.52
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Magnetic skyrmion-based synaptic devices. https://app.cochat.ai/papers/10.1088/1361-6528/aa5838
- The paper
- Huang, Y., Kang, W., Zhang, X., Zhou, Y., & Zhao, W. (2017). Magnetic skyrmion-based synaptic devices. Nanotechnology. https://doi.org/10.1088/1361-6528/aa5838
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.