Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors
Open accessAbstract
Memristors are promising building blocks for the next-generation memory and neuromorphic computing systems. Most memristors use materials that are incompatible with the silicon dominant complementary metal-oxide-semiconductor technology, and require external selectors in order for large memristor arrays to function properly. Here we demonstrate a fully foundry-compatible, all-silicon-based and self-rectifying memristor that negates the need for external selectors in large arrays. With a p-Si/SiO 2/n-Si structure, our memristor exhibits repeatable unipolar resistance switching behaviour (10 5 rectifying ratio, 10 4 ON/OFF) and excellent retention at 300 °C. We further build three-dimensinal crossbar arrays (up to five layers of 100 nm memristors) using fluid-supported silicon membranes, and experimentally confirm the successful suppression of both intra- and inter-layer sneak path currents through the built-in diodes. The current work opens up opportunities for low-cost mass production of three-dimensional memristor arrays on large silicon and flexible substrates without increasing circuit complexity.
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Use of memristive devices to emulate synapses may accelerate ANN computational tasks while reducing overall power consumption and footprint.
“Recent studies have proposed that the use of memristive devices to emulate the synapses may accelerate ANN computational tasks while reducing the overall power consumption and footprint36–42.”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [40]
The paper’s introductory passage (indexed as Abstract; page unavailable) describes memristors as promising for neuromorphic computing and notes advantages in switching speed, power consumption, and scalability. The Discussion says its selector-free arrays can increase packing density. It does not establish that using memristors as synapses accelerates ANN tasks or reduces an ANN’s overall power consumption and footprint. (LLM, conf=0.82)
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Self-citationAnalogue signal and image processing with large memristor crossbars
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- Authors
- Li, C., Han, L., Jiang, H., Jang, M.-H., Lin, P., Wu, Q., Barnell, M., Yang, J. J., Xin, H. L., & Xia, Q.
- Affiliations
- University of Massachusetts Amherst (US)
- Brookhaven National Laboratory (US)
- U.S. Air Force Research Laboratory Information Directorate (US)
- Corresponding author
- Xia, Qiangfei
- ORCID
- Venue
- Nature Communications
- Published
- 2017-06-05
- Volume
- 8
- Issue
- 1
- Pages
- 15666-15666
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5465358
- OpenAlex
- W2621466894
- Cited by
- 229
- References
- 9
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 24 · 2023: 21 · 2022: 27 · 2021: 27 · 2020: 23 · 2019: 37 · 2018: 21 · 2017: 5
- FWCI
- 9.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (ECS- 03-35765, 1253073, 0335765, DE-SC0012704)
- U.S. Department of Energy (ECS-0335765, DE‐SC0012704, Contract No: DESC0012704, No. DE-SC0012704, SC0012704.)
- Center for Hierarchical Manufacturing, National Science Foundation
- Harvard University (ECS-0335765)
- Defense Advanced Research Projects Agency (FA8750, DE-SC0012704)
- Office of Science (DE-SC-0012704, No. DE-SC0012704, ECS-0335765, Contract No. DE-SC0012704, SC0012704)
- Division of Electrical, Communications and Cyber Systems (ECS-0335765)
- Basic Energy Sciences (DE- SC0012704, SC0012704)
- Air Force Office of Scientific Research (FA9550-, -15-2-, FA9550-12, FA9550‐12‐1‐0038, FA9550‐12‐1)
- Brookhaven National Laboratory (SC0012704, No. DE-SC0012704, DE- SC0012704, Contract No. DE-SC0012704)
- Air Force Research Laboratory (DE-SC0012704, FA8750-15-2- 0044, FA9550)
- 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 › Engineering › Electrical and Electronic Engineering › Semiconductor materials and devices
- Keywords
- memristors, self-rectifying memristors, sneak path suppression, neuromorphic computing, flexible substrates
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