The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Compact Modeling of Conducting-Bridge Random-Access Memory (CBRAM)
Abstract
A physics-based compact device model is developed for the conducting-bridge random-access memory (CBRAM). By considering the dependence of ion migration velocity on the electric field, the vertical and lateral growth/dissolution dynamics for the metallic filament are investigated. Both time-dependent transient and “quasi-static” switching characteristics of the CBRAM are captured. Moreover, theI-Vcharacteristics of the CBRAM can be reproduced. By further considering the compliance effect on the size of the metallic filament, the on-state resistance modulation is fitted, and the multilevel capability is included in the model. This model is verified by the experiments data from the Ag/Ge0.3Se0.7-based CBRAM cells. This model reveals that experimentally measured switching parameters such as the threshold voltage and the cell resistance are dynamic quantities that depend on the programming duration time. The time-dependent switching process of the CBRAM is quantified, thus paving the way for a compact SPICE model for circuit simulation.
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 Memory Elements Based on Solid-State Electrolytes
Kozicki, M. N., Park, M., & Mitkova, M. (2005) · IEEE Transactions On Nanotechnology · doi:10.1109/tnano.2005.846936 · Cited by 540 · cited 1×
“The solid-electrolyte materials can be chalcogenides, e.g., Cu2 S[ 8], Ag 2 S [9], Cu:GeSe [10], and Ag:GeSe [ 11 ]; oxides, e.g., SiO2 [12], WO3 [13], and Ta2O5 [14]; nitride, e.g., Si3N4 [15]; carbide, e.g., CuC [16]; or amorphous Si [17], which acts as the resistive switching medium in between the two electrodes.”
BackgroundSchindler, C., Staikov, G., & Waser, R. (2009) · Applied Physics Letters · doi:10.1063/1.3077310 · Cited by 307
Programmable Resistance Switching in Nanoscale Two-Terminal Devices
Jo, S. H., Kim, K.-H., & Lu, W. (2009) · Nano Letters · doi:10.1021/nl803669s · Cited by 302
Understanding the switching-off mechanism in Ag+ migration based resistively switching model systems
Guo, X., Schindler, C., Menzel, S., & Waser, R. (2007) · Applied Physics Letters · doi:10.1063/1.2793686 · Cited by 248
Cited by
Zhang, Y., Mao, G.-Q., Zhao, X., Li, Y., Zhang, M., Wu, Z., Wu, W., Sun, H., Guo, Y., Wang, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27575-z · Cited by 300
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
Prezioso, M., Mahmoodi, M. R., Bayat, F. M., Nili, H., Kim, H., Vincent, A., & Strukov, D. B. (2018) · Nature Communications · doi:10.1038/s41467-018-07757-y · Cited by 215
Yuan, F., Zhang, Z., Liu, C., Zhou, F., Yau, H. M., Lu, W., Qiu, X., Wong, H.-S. P., Dai, J., & Chai, Y. (2017) · ACS Nano · doi:10.1021/acsnano.7b00783 · Cited by 99
Self-citation
Metadata
- Authors
- Yu, S., & Wong, H.-S. P.
- Venue
- IEEE Transactions on Electron Devices
- Published
- 2011-05
- Volume
- 58
- Issue
- 5
- Pages
- 1352-1360
- ISSN
- 0018-9383 (ISSN-L)
- 1557-9646
- OpenAlex
- W2121378649
- Cited by
- 239
- References
- 7
- FWCI
- 16.71
- 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: Compact Modeling of Conducting-Bridge Random-Access Memory (CBRAM). https://app.cochat.ai/papers/10.1109/ted.2011.2116120
- The paper
- Yu, S., & Wong, H.-S. P. (2011). Compact Modeling of Conducting-Bridge Random-Access Memory (CBRAM). IEEE Transactions on Electron Devices. https://doi.org/10.1109/ted.2011.2116120
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.
