Observation of Intrinsic and LED Light-Enhanced Memristor Performance in In-Plane Ferroelectric NbOI<sub>2</sub>
Two-dimensional (2D) layered ferroelectrics, as an emerging area of research, have attracted extensive attention, while memristors based on 2D ferroelectric materials are yet to be fully explored, thereby limiting their applications in modern nanoelectronics. In this work, we report the observation of intrinsic memristive behavior in a recently discovered 2D in-plane ferroelectric material, NbOI<sub>2</sub>, and the giant enhancement of the memristive performance by using light-emission diode (LED) visible light. The results show that NbOI<sub>2</sub> devices exhibit an intrinsically strong memristive response with a current on/off ratio of up to 10<sup>4</sup> and stable switching cycles, which is largely independent of the back-gate voltage. Under LED visible light illumination, the current on/off ratio in NbOI<sub>2</sub> is over 1 order of magnitude higher than that without light; meanwhile, the coercive field is significantly reduced to less than ∼1.22 kV/cm, much lower than other 2D ferroelectric-based memristors. Interestingly, both the intrinsic and light-enhanced resistive switching phenomena only occur along the in-plane <i>b</i>-axis direction, indicating that the memristive behavior in NbOI<sub>2</sub> is driven by electric field-induced and optical field-enhanced ferroelectric polarization switching mechanisms, as evidenced by a combined orientation-dependent electrical/optoelectrical measurement and lateral piezoresponse force microscopy analysis. Our study not only provides a materials strategy based on 2D in-plane ferroelectrics for designing memristive devices but also offers a simple optical method to enhance device performance, facilitating their implementation in next-generation nanoelectronics and optoelectronics.
Authors
- Andrey Novitsky (ORCID: https://orcid.org/0000-0001-9553-7318)
- Juhe Liu
- А. Л. Толстик (ORCID: https://orcid.org/0000-0003-4953-4890)
- Huolin Huang (ORCID: https://orcid.org/0000-0003-4721-9459)
- Yunhao Tong
- Fanyi Kong (ORCID: https://orcid.org/0000-0003-4935-8831)
- Xue Han (ORCID: https://orcid.org/0000-0001-9525-1176)
- Zheng Hao (ORCID: https://orcid.org/0009-0002-7796-3625)
- Lujun Pan (ORCID: https://orcid.org/0000-0003-3666-408X)
- К. В. Козадаев (ORCID: https://orcid.org/0000-0001-6263-8024)
- Dawei Li (ORCID: https://orcid.org/0000-0001-6967-4968)
- Haoran Li (ORCID: https://orcid.org/0000-0001-8183-0329)
- Changsen Sun (ORCID: https://orcid.org/0000-0001-8478-0742)
- Gaolei Zhao
- Jichang Zhang (ORCID: https://orcid.org/0000-0003-2189-0172)
- Hong Li (ORCID: https://orcid.org/0009-0005-6299-1254)
- Jiabin Liu
Institutions
- Dalian University of Technology (CN)
- Belarusian State University (BY)
Publication Details
- Journal
- ACS Nano
- Published
- 2025-07-26
- DOI
- https://doi.org/10.1021/acsnano.5c07236
- Citations
- 4
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.92
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Liaoning Province
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities
- Chunhui Project Foundation of the Education Department of China