The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Tuning Ionic Transport in Memristive Devices by Graphene with Engineered Nanopores
Abstract
Memristors, based on inherent memory effects in simple two-terminal structures, have attracted tremendous interest recently for applications ranging from nonvolatile data storage to neuromorphic computing based on non-von Neumann architectures. In a memristor, the ability to modulate and retain the state of an internal variable leads to experimentally observed resistive switching (RS) effects. Such phenomena originate from internal, microscopic ionic migration and associated electrochemical processes that modify the materials' electrical and other physical properties. To optimize the device performance for practical applications with large-size arrays, controlling the internal ionic transport and redox reaction processes thus becomes a necessity, ideally at the atomic scale. Here we show that the RS characteristics in tantalum-oxide-based memristors can be systematically tuned by inserting a graphene film with engineered nanopores. Graphene, with its atomic thickness and excellent impermeability and chemical stability, can be effectively integrated into the device stack and can offer unprecedented capabilities for the control of ionic dynamics at the nanoscale. In this device structure, the graphene film effectively blocks ionic transport and redox reactions; thereby the oxygen vacancies required during the RS process are allowed to transport only through the engineered nanosized openings in the graphene layer, leading to effective modulation of the device performance by controlling the nanopore size in graphene. The roles of graphene as an ion-blocking layer in the device structure were further supported by transmission electron microscopy, energy-dispersive X-ray spectroscopy, and atomistic simulations based on first-principles calculations.
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
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
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
Self-citationMemristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885
Training 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
Memristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987
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
‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature · doi:10.1038/nature08940 · Cited by 1,929
Cited by
The future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138
Self-citationA 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
Choi, S., Tan, S. H., Li, Z., Kim, Y., Choi, C., Chen, P.-Y., Yeon, H., Yu, S., & Kim, J. (2018) · Nature Materials · doi:10.1038/s41563-017-0001-5 · Cited by 690
Recommended Methods to Study Resistive Switching Devices
Lanza, M., Wong, H. ‐. P., Pop, E., Ielmini, D., Strukov, D., Regan, B. C., Larcher, L., Villena, M. A., Yang, J. J., Goux, L., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800143 · Cited by 674
Self-citationTian, H., Zhao, L., Wang, X., Yeh, Y.-W., Yao, N., Rand, B. P., & Ren, T.-L. (2017) · ACS Nano · doi:10.1021/acsnano.7b05726 · Cited by 371
Metadata
- Authors
- Lee, J., Du, C., Sun, K., Kioupakis, E., & Lu, W. D.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-03-10
- Volume
- 10
- Issue
- 3
- Pages
- 3571-3579
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2296420188
- Cited by
- 176
- References
- 9
- FWCI
- 8.99
- 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
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.