Regulation of vacancy formation in h-BN via carbon doping toward improved resistive switching
Hexagonal boron nitride (h-BN) exhibits promising potential in resistive switching (RS) applications, yet the performance variability remains a major obstacle, primarily due to the uncontrollable nature of intrinsic defects. Carbon doping can offer a potential means to regulate defect types and their spatial distribution. However, the microscopic mechanisms by which carbon dopants regulate defects in h-BN remain unclear. In this work, the modulation of the RS behavior in h-BN-based resistive random-access memory (RRAM) by carbon dopants is studied using first-principles calculations. The results indicate that carbon substituted for boron (CB) not only promotes the formation of boron vacancies (VB), but its interaction with vacancies further facilitates vacancy clustering around the dopant. Notably, interlayer interactions depend on defect type, with VB in adjacent layers exhibiting mutual repulsion, whereas nitrogen vacancies (VN) attract and stabilize VB. Climbing image nudged elastic band calculations demonstrate that RS does not rely on VB migration but is mediated by boron–nitrogen divacancy (VBN) enabled conductive bridge formation (−1q/−2q charge states) and breaking (0q/+1q charge states). Nonequilibrium Green's function transport calculations verify a pronounced enhancement of the current upon conductive bridge formation. This work provides fundamental theoretical insights for defect engineering in the development of h-BN-based RRAM.
Authors
- Yang Ge (ORCID: https://orcid.org/0000-0003-3723-5447)
- Shengbo Sang (ORCID: https://orcid.org/0000-0003-3011-7632)
- Xiaojie Chai (ORCID: https://orcid.org/0000-0002-6022-8264)
- Yan Liu (ORCID: https://orcid.org/0009-0003-2341-6826)
- Yifan Zhang (ORCID: https://orcid.org/0009-0004-5582-6149)
- Guangyu Yan (ORCID: https://orcid.org/0009-0001-2872-7725)
Institutions
- Shanxi University (CN)
- Taiyuan University of Science and Technology (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2026-07-27
- DOI
- https://doi.org/10.1063/5.0323857
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00