High‐Performing Nanofiber Memristor via Field‐Induced Ion Migration Concentration at Highly‐Curved Interwoven Interface
Memristors with high computing capabilities hold significant potential as processing devices in the era of information explosion, but their applications are largely hindered by the trade-off between memristive performance and operation uniformity. Extensive efforts are devoted to designing or regulating memristive layers of memristors to improve device performance, while few studies focused on the interface structure at cross points of memristors. Herein, inspired by the tip effect, a nanofiber memristor with highly-curved interwoven interface is designed by assembling nanofiber electrodes using dielectrophoretic method. The nanofiber memristor shows high operation uniformity with an ultralow set voltage standard deviation of 0.014 V, which is even superior to that of state-of-the-art oxide-based planar memristors. Experimental and simulation analysis reveals that the highly-curved interface can efficiently concentrate electric field distribution and confine the switching region, thus facilitating the migration of silver ions and suppressing the formation of random conductive filaments. Nanofiber memristor arrays with good device reproducibility are further integrated for fundamental logic gate circuits like NOT, AND, and OR. This work offers a new insight for constructing high-performing nanoelectronics.
Authors
- Zhuming Wang (ORCID: https://orcid.org/0000-0002-2230-5716)
- Xufeng Zhou (ORCID: https://orcid.org/0000-0003-3049-0306)
- Xuemei Sun (ORCID: https://orcid.org/0000-0002-2583-8593)
- Peining Chen (ORCID: https://orcid.org/0000-0003-0566-1660)
- Zhe Wei Yang (ORCID: https://orcid.org/0000-0003-0753-3902)
- Yue Liu (ORCID: https://orcid.org/0000-0003-1021-8970)
- Chen Wang (ORCID: https://orcid.org/0000-0002-3777-5454)
- Lin Chen (ORCID: https://orcid.org/0009-0006-4586-2706)
- Yuanhang Zhang (ORCID: https://orcid.org/0000-0002-5415-3307)
- Jialin Meng (ORCID: https://orcid.org/0000-0002-2166-3220)
- Huisheng Peng
Institutions
- Fudan University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Shanghai Fudan Microelectronics (China) (CN)
- State Key Laboratory of ASIC and System
Publication Details
- Journal
- Small
- Published
- 2025-02-16
- DOI
- https://doi.org/10.1002/smll.202409951
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Science and Technology Commission of Shanghai Municipality