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RRAM-based synapse for neuromorphic system with pattern recognition function
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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Array of Cointegrated Transistor‐Based Artificial Neurons and Synapses for Neuromorphic Computing
Lee, S., Seo, S., Jeong, H., Han, J., Kim, Y., Yoo, D.-E., Son, J.-H., Yu, J., Choi, S., & Choi, Y. (2026) · Small · doi:10.1002/smll.74638
Hwang, E., Myeong, I., & Kim, G. (2026) · Silicon · doi:10.1007/s12633-026-03764-7
Buczek, M., Dossayev, I. T., Wittberg, C., Liu, Y., Goß, K., Mueller, D. N., Moos, Z., Liu, Z., Bittkau, K., Sultani, M. H., et al. (2026) · Advanced Electronic Materials · doi:10.1002/aelm.202500556
Hwang, E., Myeong, I., Kim, S., & Kim, G. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70597
Self-citation
Metadata
- 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)
- Venue
- 2012 International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 10.2.1-10.2.4
- ISSN
- 0163-1918 (ISSN-L)
- 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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