Cited by 192Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

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., Shin, J., Lee, D., Choi, G., Woo, J., Cha, E., Jang, J., Park, C., Jeon, M., Lee, B., Lee, B. H., Hwang, H.·2012·2012 International Electron Devices Meeting

DOI 10.1109/iedm.2012.6479016

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Abstract

Feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic RRAM array and CMOS-based neuron chip has been experimentally demonstrated. Learning capability of a neuromorphic system comprising RRAM synapses and CMOS neurons has been confirmed experimentally, for the first time.

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4 of 4 references verified0 citation errors · 0 not checkable

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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Snider, G. S. (2007) · Nanotechnology · doi:10.1088/0957-4484/18/36/365202 · Cited by 264

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Research works printed but not linked: 9

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jung, 2011, IEDM Tech Dig, 59
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suri, 2011, IEDM Tech Dig, 59
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Sorted by citations

Synaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Wong, H. ‐. P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,257
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
“On the other hand, the interfacial device typically shows both gradual SET and RESET processes, as demonstrated in Ag:a-Si [77], PrCaMnO3 (PCMO) [78], [79], and TaOx/TiO2 [80], [81] devices.”
Background
Neuromorphic computing using non-volatile memory
Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,196
“Continuously increasing potentiation (depression) behaviors were observed upon iterative programming with identical pulses of negative (positive) bias [169,170].”
BackgroundSelf-citation
Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications
Zahoor, F., Zulkifli, T. Z. A., & Khanday, F. A. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-03299-9 · Cited by 985
An Artificial Neuron Based on a Threshold Switching Memristor
Zhang, X., Wang, W., Liu, Q., Zhao, X., Wei, J., Cao, R., Yao, Z., Zhu, X., Zhang, F., Lv, H., et al. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2782752 · Cited by 407
“memristors present promising characteristics for developing artificial synapses to realize brain-inspired computing [1]–[3].”
Background

See all 192

Neuromorphic System With Novel Electronic Synapses Based On Rram Devices
Zhang, Z. (2015) · International Journal of Electrical Electronics and Data Communication · doi:10.18479/ijeedc/2015/v3i8/48345
Design and Hardware Implementation of Neuromorphic Systems With RRAM Synapses and Threshold-Controlled Neurons for Pattern Recognition
Jiang, Y., Huang, P. C., Zhu, D., Zhou, Z., Han, R., Liu, L., Liu, X., & Kang, J. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2018.2812419 · Cited by 57
Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning
Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Z., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · Symposium on VLSI Technology · doi:10.1109/vlsit.2016.7573432 · Cited by 40
Spiking neural network with 1T-1R RRAM synapses and CMOS LIF neurons for in-situ learning and pattern recognition
Nivetha, T., Bindu, B., & Kamsani, N. A. (2025) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2025.156145
RRAM-based synapse devices for neuromorphic systems
Moon, K., Lim, S., Park, J., Sung, C., Oh, S., Woo, J., Lee, J., & Hwang, H. (2018) · Faraday Discussions · doi:10.1039/c8fd00127h · Cited by 241
RRAM-based pattern recognition system with locally inhibited post-neurons
Zhou, Z., Huang, P., Jiang, Y., Chen, Z., Liu, C., Liu, L., Liu, X., & Kang, J. (2017) · Silicon Nanoelectronics Workshop (SNW) · doi:10.23919/snw.2017.8242340
Circuit design of RRAM-based neuromorphic hardware systems for classification and modified Hebbian learning
Jiang, Y., Huang, P. C., Zhou, Z., & Kang, J. (2019) · Science China Information Sciences · doi:10.1007/s11432-018-9863-6 · Cited by 11
Online training on RRAM based neuromorphic network: Experimental demonstration and operation scheme optimization
Yao, P., Wu, H., Gao, B., Deng, N., Yu, S., & Qian, H. (2017) · IEEE Electron Devices Technology & Manufacturing Conference (EDTM) · doi:10.1109/edtm.2017.7947592 · Cited by 6
Improved Synaptic Behavior Under Identical Pulses Using AlO x /HfO 2 Bilayer RRAM Array for Neuromorphic Systems
Woo, J., Moon, K., Song, J., Lee, S., Kwak, M., Park, J., & Hwang, H. (2016) · IEEE Electron Device Letters · doi:10.1109/led.2016.2582859 · Cited by 512
Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM
Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Z.-Q., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2526647 · Cited by 256

Authors: Park, S., Kim, H., Choo, M., Noh, J., Sheri, A., Jung, S., Seo, K., Park, J., Kim, S., Lee, W., Shin, J., Lee, D., Choi, G., Woo, J., Cha, E., Jang, J., Park, C., Jeon, M., Lee, B., Lee, B. H., & Hwang, H.
Affiliations: Gwangju Institute of Science and Technology (KR)
Pohang University of Science and Technology (KR)
ORCID: Park, S.
Kim, H.
Sheri, A.
Shin, J.
Woo, J.
Cha, E.
Jang, J.
Jeon, M.
Lee, B.
Lee, B. H.
Hwang, H.
Venue: 2012 International Electron Devices Meeting
Publisher: IEEE
Published: 2012-12
Pages: 10.2.1-10.2.4
ISSN: 0163-1918 (ISSN-L)
DOI: 10.1109/iedm.2012.6479016
OpenAlex: W1993932725
Cited by: 191
References: 4
Citations by year: 2026: 5 · 2025: 2 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 21 · 2020: 22 · 2019: 18 · 2018: 24 · 2017: 25 · 2016: 18 · 2015: 10 · 2014: 8 · 2013: 4
FWCI: 8.99
Type: conference paper
Language: en
Open access: closed
Locations: Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
Keywords: resistive random access memory, neuromorphic systems, pattern recognition, learning ability

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RRAM-based synapse for neuromorphic system with pattern recognition function

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