The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device
Open accessAbstract
A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaO x/TiO 2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems.
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
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
Memristive 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 2×
“Discussion The I-V characteristics of general memristive devices are described using the following two coupled equations [3]:”
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
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 · cited 1×
“These features outperform those of other electronic synapse candidates based on phase-change memory [16,17], ferroelectric memory [18], and floating-gate devices [19].”
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
Synaptic behaviors and modeling of a metal oxide memristive device
Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., & Lu, W. (2011) · Applied Physics A · doi:10.1007/s00339-011-6296-1 · Cited by 432
A single-transistor silicon synapse
Diorio, C., Hasler, P., Minch, A., & Mead, C. A. (1996) · IEEE Transactions on Electron Devices · doi:10.1109/16.543035 · Cited by 360 · cited 1×
“These features outperform those of other electronic synapse candidates based on phase-change memory [16,17], ferroelectric memory [18], and floating-gate devices [19].”
Origin of the Ultra‐nonlinear Switching Kinetics in Oxide‐Based Resistive Switches
Menzel, S., Waters, M., Marchewka, A., Böttger, U., Dittmann, R., & Waser, R. (2011) · Advanced Functional Materials · doi:10.1002/adfm.201101117 · Cited by 348
Suri, M., Bichler, O., Querlioz, D., Cueto, O., Perniola, L., Sousa, V., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2011) · 2011 International Electron Devices Meeting · doi:10.1109/iedm.2011.6131488 · Cited by 314
Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H.-S. P. (2012) · 2012 International Electron Devices Meeting · doi:10.1109/iedm.2012.6479018 · Cited by 213
RRAM-based synapse for neuromorphic system with pattern recognition function
Park, S., Kim, H., Choo, M., Noh, J., Sheri, A., Jung, S., Seo, K., Park, J., Kim, S., Lee, W., et al. (2012) · 2012 International Electron Devices Meeting · doi:10.1109/iedm.2012.6479016 · Cited by 191 · cited 1×
“However, exotic materials, such as praseodymium calcium manganese oxide (PCMO) [12,13], and processes incompatible with semiconductor fabrication, such as sol-gel [14], are often required for fabricating these devices, thus posing additional challenges for high-density integration.”
Cited by
Neuromorphic computing using non-volatile memory
Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K., Ishii, M., Narayanan, P., Fumarola, A., et al. (2017) · Advances in Physics: X · doi:10.1080/23746149.2016.1259585 · Cited by 1,107
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
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
Learning through ferroelectric domain dynamics in solid-state synapses
Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., Girod, S., Carrétéro, C., Garcia, K., Xavier, S., et al. (2017) · Nature Communications · doi:10.1038/ncomms14736 · Cited by 600
Multibit memory operation of metal-oxide bi-layer memristors
Stathopoulos, S., Khiat, A., Trapatseli, M., Cortese, S., Serb, A., Valov, I., & Prodromakis, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-17785-1 · Cited by 312
Metadata
- Authors
- Wang, Y.-F., Lin, Y.-C., Wang, I.-T., Lin, T.-P., & Hou, T.-H.
- Venue
- Scientific Reports
- Published
- 2015-05-08
- Volume
- 5
- Issue
- 1
- Pages
- 10150-10150
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W1142261130
- Cited by
- 188
- References
- 13
- FWCI
- 9.14
- Type
- journal article
- Language
- en
- Open access
- gold
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.
