The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Organic Resistive Memory Devices: Performance Enhancement, Integration, and Advanced Architectures
Abstract
In recent years, organic resistive memory devices in which active organic materials possess at least two stable resistance states have been extensively investigated for their promising memory potential. From the perspective of device fabrication, their advantages include simple device structures, low fabrication costs, and printability. Furthermore, their exceptional electrical performances such as a nondestructive reading process, nonvolatility, a high ON/OFF ratio, and a fast switching speed meet the requirements for viable memory technologies. Full understanding of the underlying physics behind the interesting phenomena is still challenging. However, many studies have provided useful insights into scientific and technical issues surrounding organic resistive memory. This Feature Article begins with a summary on general characteristics of the materials, device structures, and switching mechanisms used in organic resistive devices. Strategies for performance enhancement, integration, and advanced architectures in these devices are also presented, which may open a way toward practically applicable organic memory devices.
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Cites
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Cited by
The Future of Memristors: Materials Engineering and Neural Networks
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Status and prospects of ZnO-based resistive switching memory devices
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Self-citationReal-time finger motion recognition using skin-conformable electronics
Cho, H., Lee, I., Jang, J., Kim, J.-H., Lee, H., Park, S., & Wang, G. (2023) · Nature Electronics · doi:10.1038/s41928-023-01012-z · Cited by 87
Metadata
- Authors
- Cho, B., Song, S., Ji, Y., Kim, T., & Lee, T.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2011-07-08
- Volume
- 21
- Issue
- 15
- Pages
- 2806-2829
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W1991578486
- Cited by
- 489
- References
- 79
- FWCI
- 23.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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