Hexagonal Boron Nitride Memristor with Ultralow Operating Voltage Enabled by Noninvasive vdWs Electrode
Future electronics calls for energy efficiency of nonvolatile memristor. An effective route to implement a low-power memristor is to manipulate the ionic dynamics in the resistive switching medium. The roughness of the interface between electrode and medium layer determines the initial ions distribution. However, conventional metal deposition methods create surface roughness that damages the medium layer. The random ion diffusion derived from the intrusive rough electrode results in unstable conductive filaments and degrades memristor energy efficiency. Here, we demonstrate a new strategy to integrate noninvasive rough metal electrode for ultralow-voltage, robust nonvolatile memristor. The rough electrode is prefabricated and laminated onto the h-BN layer. The improved noninvasive rough metal/dielectric interface facilitates ultraconfined conductive path formation, leading to superb memristive performance with SET voltage down to 41 mV. The integration approach is demonstrated to be viable to realize 2D dielectric memristor with ultralow operating voltage, pushing the boundary of device energy efficiency.
Authors
- Xingyu Zhai (ORCID: https://orcid.org/0000-0002-3035-9407)
- Yuda Zhao (ORCID: https://orcid.org/0000-0003-2748-0537)
- Jiayang Hu (ORCID: https://orcid.org/0000-0001-6707-1549)
- Bin Yu (ORCID: https://orcid.org/0000-0003-0237-6311)
- Haohan Chen (ORCID: https://orcid.org/0000-0002-6515-4954)
- Jialei Miao (ORCID: https://orcid.org/0000-0002-6728-0225)
- Yu Kang (ORCID: https://orcid.org/0009-0000-6767-1659)
- Zhixiang Zhang (ORCID: https://orcid.org/0000-0001-9463-3981)
- Huan Hu (ORCID: https://orcid.org/0000-0002-1317-5470)
- Wenchao Chen (ORCID: https://orcid.org/0009-0007-4300-0827)
- Dong Pu
- Quan Yang (ORCID: https://orcid.org/0000-0003-4308-3646)
Institutions
- University of Illinois Urbana-Champaign (US)
- Zhejiang University (CN)
Publication Details
- Journal
- ACS Materials Letters
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1021/acsmaterialslett.5c00380
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- China Association for Science and Technology
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province