Low-Power, Self-Rectifying, and Forming-Free Memristor with an Asymmetric Programing Voltage for a High-Density Crossbar Application
Abstract
A Pt/NbO x /TiO y /NbO x /TiN stack integrated on a 30 nm contact via shows a programming current as low as 10 nA and 1 pA for the set and reset switching, respectively, and a self-rectifying ratio as high as ∼10 5, which are suitable characteristics for low-power memristor applications. It also shows a forming-free characteristic. A charge-trap-associated switching model is proposed to account for this self-rectifying memrisive behavior. In addition, an asymmetric voltage scheme (AVS) to decrease the write power consumption by utilizing this self-rectifying memristor is also described. When the device is used in a 1000 × 1000 crossbar array with the AVS, the programming power can be decreased to 8.0% of the power consumption of a conventional biasing scheme. If the AVS is combined with a nonlinear selector, a power consumption reduction to 0.31% of the reference value is possible.
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The charge trap memristor is highly compatible with the conventional CMOS process.
“the device is highly compatible with the conventional CMOS process.”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [21]
The self-rectifying characteristics of the charge trap memristor enable large-array operation without complex transistor or selector processes.
“the self-rectifying characteristics of the device enable large-array operation without complex transistor or selector processes”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [21]
A charge trap memristor has a low current analog operation capability and electroforming-free characteristics, allowing high reliability and uniformity.
“It has a low current analog operation capability and electroforming-free characteristics, allowing high reliability and uniformity.”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [21]
Inserting a nonlinear selector device in series with a self-rectifying charge trap memristor can reduce total power consumption by suppressing unnecessary power consumption from half-selected cells.
“It was demonstrated that inserting a nonlinear selector device in series with the self‐rectifying CTM could reduce the total power consumption by suppressing the unnecessary power consumption from half‐selected cells.[ 7 ]”
From Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware (2023) · cites this paper as [7]
Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,744 · see citing works
Self-citationNanoscale 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 · see citing works
Memristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885 · see citing works
Self-citationMemristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987 · see citing works
Self-citationKim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2012) · Nano Letters · doi:10.1021/nl203687n · Cited by 924 · see citing works
Self-citationHigh-Density Crossbar Arrays Based on a Si Memristive System
Jo, S. H., Kim, K.-H., & Lu, W. (2009) · Nano Letters · doi:10.1021/nl8037689 · Cited by 574 · see citing works
Self-citationYoon, J. H., Song, S. J., Yoo, I., Seok, J. Y., Yoon, K. J., Kwon, D. E., Park, T. H., & Hwang, C. S. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201400064 · Cited by 277 · see citing works
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Analogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292 · see citing works
Self-citationSelf-selective van der Waals heterostructures for large scale memory array
Sun, L., Zhang, Y., Han, G., Hwang, G., Jiang, J., Joo, B., Watanabe, K., Taniguchi, T., Kim, Y.-M., Yu, W. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11187-9 · Cited by 248 · see citing works
Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays
Li, Y., & Ang, K.-W. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202000137 · Cited by 220 · see citing works
Memristive Crossbar Arrays for Storage and Computing Applications
Li, H., Wang, S., Zhang, X., Wang, W., Yang, R., Sun, Z., Feng, W., Lin, P., Wang, Z., Sun, L., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100017 · Cited by 205 · see citing works
Jeon, K., Ryu, J. J., Im, S., Seo, H. K., Eom, T., Ju, H., Yang, M. K., Jeong, D. S., & Kim, G. H. (2024) · Nature Communications · doi:10.1038/s41467-023-44620-1 · Cited by 108 · see citing works
Artificial Neural Networks Based on Memristive Devices: From Device to System
Huang, H.-M., Wang, Z., Wang, T., Xiao, Y., & Guo, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000149 · Cited by 82 · see citing works
Kim, G., Son, S., Song, H., Jeon, J. B., Lee, J., Cheong, W. H., Choi, S., & Kim, K. M. (2023) · Advanced Science · doi:10.1002/advs.202205654 · Cited by 72 · see citing works
Self-citationKim, G. S., Song, H., Lee, Y. K., Kim, J. H., Kim, W., Park, T. H., Kim, H. J., Min Kim, K., & Hwang, C. S. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b16499 · Cited by 64 · see citing works
Self-citationPark, J., Kim, S., Song, M. S., Youn, S., Kim, K., Kim, T.-H., & Kim, H. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c13775 · Cited by 56 · see citing works
Self-citationSrivastava, S., Thomas, J. P., & Leung, K. T. (2019) · Nanoscale · doi:10.1039/c9nr06403f · Cited by 27 · see citing works
Lee, Y., Rhee, H., Kim, G., Cheong, W. H., Kim, D. H., Song, H., Kay, S. N., Lee, J., & Kim, K. M. (2025) · Nature Communications · doi:10.1038/s41467-025-59589-2 · Cited by 19 · see citing works
Self-citationSong, H., Kim, Y. S., Park, J., & Kim, K. M. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800740 · Cited by 18 · see citing works
Self-citationNg, S., John, R. A., Yang, J.-T., & Mathews, N. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00002 · Cited by 18 · see citing works
Self-citationSong, H., An, J., Son, S., Kim, Y. S., Park, J., Jeon, J. B., Kim, G., & Kim, K. M. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000014 · Cited by 4 · see citing works
Self-citation
- Authors
- Kim, K. M., Zhang, J., Graves, C., Yang, J. J., Choi, B. J., Hwang, C. S., Li, Z., & Williams, R. S.
- Affiliations
- Hewlett-Packard (United States) (US)
- University of Massachusetts Amherst (US)
- Seoul National University of Science and Technology (KR)
- Seoul National University (KR)
- Corresponding author
- Hwang, Cheol Seong
- Williams, R. Stanley
- ORCID
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-10-17
- Volume
- 16
- Issue
- 11
- Pages
- 6724-6732
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2521878335
- Cited by
- 246
- References
- 7
- Citations by year
- 2026: 15 · 2025: 30 · 2024: 39 · 2023: 19 · 2022: 17 · 2021: 36 · 2020: 27 · 2019: 26 · 2018: 16 · 2017: 19 · 2016: 1
- FWCI
- 9.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nano Letters (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF-2014R1A2A1A10052979)
- Ministry of Science, ICT and Future Planning (2012040157)
- 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, forming-free switching, low-power memory, crossbar arrays, resistive switching
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