Ultra-Low-Energy Three-Dimensional Oxide-Based Electronic Synapses for Implementation of Robust High-Accuracy Neuromorphic Computation Systems
Abstract
Neuromorphic computing is an attractive computation paradigm that complements the von Neumann architecture. The salient features of neuromorphic computing are massive parallelism, adaptivity to the complex input information, and tolerance to errors. As one of the most crucial components in a neuromorphic system, the electronic synapse requires high device integration density and low-energy consumption. Oxide-based resistive switching devices have been shown to be a promising candidate to realize the functions of the synapse. However, the intrinsic variation increases significantly with the reduced spike energy due to the reduced number of oxygen vacancies in the conductive filament region. The large resistance variation may degrade the accuracy of neuromorphic computation. In this work, we develop an oxide-based electronic synapse to suppress the degradation caused by the intrinsic resistance variation. The synapse utilizes a three-dimensional vertical structure including several parallel oxide-based resistive switching devices on the same nanopillar. The fabricated three-dimensional electronic synapse exhibits the potential for low fabrication cost, high integration density, and excellent performances, such as low training energy per spike, gradual resistance transition under identical pulse training scheme, and good repeatability. A pattern recognition computation is simulated based on a well-known neuromorphic visual system to quantify the feasibility of the three-dimensio…
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Many studies have reported that only two discrete states are demonstrated during potentiation, whereas depression demonstrates multiple states.
“However, many studies have reported that only two discrete states are demonstrated during potentiation, whereas depression demonstrates multiple states [9-11].”
From Linking Conductive Filament Properties and Evolution to Synaptic Behavior of RRAM Devices for Neuromorphic Applications (2017) · cites this paper as [10]
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Self-citation
- Authors
- Gao, B., Bi, Y., Chen, H.-Y., Liu, R., Huang, P., Chen, B., Liu, L., Liu, X., Yu, S., Wong, H.-S. P., & Kang, J.
- Affiliations
- Peking University (CN)
- Stanford University (US)
- Arizona State University (US)
- Corresponding author
- Gao, Bin
- Liu, Lifeng
- Kang, Jinfeng
- ORCID
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-02
- Volume
- 8
- Issue
- 7
- Pages
- 6998-7004
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2328903318
- Cited by
- 188
- References
- 8
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 14 · 2022: 15 · 2021: 19 · 2020: 23 · 2019: 29 · 2018: 26 · 2017: 22 · 2016: 17 · 2015: 9
- FWCI
- 8.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Nano (publishedVersion)
- PubMed (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
- electronic synapses, neuromorphic computing, oxygen vacancies, conductive filaments, resistance variation, pattern recognition
- MeSH terms
- Oxides, Synapses, Neural Networks, Computer, Biomimetics, Nanotechnology
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