The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation
Abstract
Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.
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
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
Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T., & Tsai, M.-J. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190369 · Cited by 2,757
Self-citationNeuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Lee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
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
Self-citationShort-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor
Chang, T., Jo, S.-H., & Lu, W. (2011) · ACS Nano · doi:10.1021/nn202983n · Cited by 1,058
Indiveri, G., Chicca, E., & Douglas, R. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.860850 · Cited by 1,009
Elements of Artificial Neural Networks
Mehrotra, K., Mohan, C., & Ranka, S. (1996) · The MIT Press eBooks · doi:10.7551/mitpress/2687.001.0001 · Cited by 912
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805
Self-citation
Research works printed but not linked: 10
- Ananthanarayanan R., 2009, ACM/IEEE Conference High Performance Networking Computing, Storage and Analysis
- Merolla P., 2011, IEEE Custom Integrated Circuits Conference (CICC)
- Snider G. S., 2008, ACM/IEEE International Symposium on Nanoscale Architectures (NANOARCH), 85
- Suri M., 2011, IEEE International Electron Devices Meeting (IEDM), 79
- Kuzum D., 2011, IEEE International Electron Devices Meeting (IEDM), 693
- Wu Y., 2012, IEEE International Memory Workshop (IMW)
- Lee H. Y., 2010, IEEE International Electron Devices Meeting (IEDM), 460
- Govoreanu B., 2011, IEEE International Electron Devices Meeting (IEDM), 729
- Sheu S.‐S., 2011, IEEE International Solid‐State Circuits Conference (ISSCC), 200
- Kawahara A., 2012, IEEE International Solid‐State Circuits Conference (ISSCC), 432
Cited by
In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
“Cumulative change of resistance has been adopted for analog synapses in artificial neural networks using both filamentary [67-69] and interface-switching RRAM devices [70], as well as ferroelectric devices [71] and domain-wall STT-MRAM devices [72].”
MethodSelf-citationSynaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Philip Wong, H.-S. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,258
Self-citationNeuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Self-citationFully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106
Face classification using electronic synapses
Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications · doi:10.1038/ncomms15199 · Cited by 909
Self-citation
Metadata
- Authors
- Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H. ‐. P.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2013-01-27
- Volume
- 25
- Issue
- 12
- Pages
- 1774-1779
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2087748124
- Cited by
- 527
- References
- 21
- FWCI
- 22.17
- Type
- journal article
- Language
- en
- Open access
- closed
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: A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation. https://app.cochat.ai/papers/10.1002/adma.201203680
- The paper
- Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H. ‐. P. (2013). A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation. Advanced Materials. https://doi.org/10.1002/adma.201203680
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.
