The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Boundary Condition-Based Approach to the Modeling of Memristor Nanostructures
Abstract
A deep theoretical discussion proves that in Joglekar's and Biolek's models the memductance-flux relation of a memristor driven by a sign-varying voltage source may only exhibit single-valuedness and multi-valuedness respectively. This manuscript derives a novel boundary condition-based Model for memristor nanostructures. Unlike previous models, the proposed one allows for closed-form solutions. More importantly, subject to the nonlinear behavior under exam, this model enables a suitable tuning of boundary conditions, which may result in the detection of both single-valued and multi-valued memductance-flux relations under certain sign-varying inputs of interest. The large class of modeled dynamics include all behaviors reported in the legendary paper revealing the existence of memory-resistance at the nano scale.
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
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“It was theoretically introduced in 1971 [1] and then classified within the larger class of memristive systems [2].”
BackgroundMemristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604 · cited 1×
“It was theoretically introduced in 1971 [1] and then classified within the larger class of memristive systems [2].”
BackgroundResistance switching memories are memristors
Chua, L. (2011) · Applied Physics A · doi:10.1007/s00339-011-6264-9 · Cited by 1,434
How We Found The Missing Memristor
Williams, R. (2008) · IEEE Spectrum · doi:10.1109/mspec.2008.4687366 · Cited by 697 · cited 4×
“However, it was only after the 2008 first-ever recognition of memristor behavior at the nano-scale from Hewlett-Packard (HP) engineer Williams [3], [4] that it started to receive significant attention for the large spectrum of opportunities it opens up in integrated circuit (IC) design, mostly for the realization of l…”
BackgroundInfluentialHigh-Density Crossbar Arrays Based on a Si Memristive System
Jo, S. H., Kim, K.-H., & Lu, W. (2009) · Nano Letters · doi:10.1021/nl8037689 · Cited by 574
Spike-timing-dependent learning in memristive nanodevices
Snider, G. S. (2008) · 2008 IEEE International Symposium on Nanoscale Architectures · doi:10.1109/nanoarch.2008.4585796 · Cited by 341 · cited 1×
“However, it was only after the 2008 first-ever recognition of memristor behavior at the nano-scale from Hewlett-Packard (HP) engineer Williams [3], [4] that it started to receive significant attention for the large spectrum of opportunities it opens up in integrated circuit (IC) design, mostly for the realization of l…”
Background
Cited by
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training
Soudry, D., Di Castro, D., Gal, A., Kolodny, A., & Kvatinsky, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2383395 · Cited by 271
Memristive diode bridge with LCR filter
Corinto, F., & Ascoli, A. (2012) · Electronics Letters · doi:10.1049/el.2012.1480 · Cited by 189
Self-citationGST-memristor-based online learning neural networks
Xiao, S., Xie, X., Wen, S., Zeng, Z., Huang, T., & Jiang, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.014 · Cited by 45
An Advection-Diffusion Model for the Vacancy Migration Memristor
Abraham, I. (2016) · IEEE Access · doi:10.1109/access.2016.2621721 · Cited by 8
Analysis of a serial circuit with two memristors and voltage source at sine and impulse regime
Mladenov, V. M., & Kirilov, S. M. (2012) · 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications · doi:10.1109/cnna.2012.6331476 · Cited by 6
Metadata
- Authors
- Corinto, F., & Ascoli, A.
- Venue
- IEEE Transactions on Circuits and Systems I: Regular Papers
- Published
- 2012-11
- Volume
- 59
- Issue
- 11
- Pages
- 2713-2726
- ISSN
- 1549-8328 (ISSN-L)
- 1558-0806
- OpenAlex
- W2059888459
- Cited by
- 163
- References
- 7
- FWCI
- 14.24
- 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 Boundary Condition-Based Approach to the Modeling of Memristor Nanostructures. https://app.cochat.ai/papers/10.1109/tcsi.2012.2190563
- The paper
- Corinto, F., & Ascoli, A. (2012). A Boundary Condition-Based Approach to the Modeling of Memristor Nanostructures. IEEE Transactions on Circuits and Systems I: Regular Papers. https://doi.org/10.1109/tcsi.2012.2190563
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.