Stress-engineered 0.34 nm graphene memristors for low-power probabilistic computing
- DOI
- 10.1038/s41467-026-77789-2
- Published
- 2026-09-23
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-026-77789-2,
title = {Stress-engineered 0.34 nm graphene memristors for low-power probabilistic computing},
author = {Yanming Liu and Jinzhu Li and Zhoujie Pan and Hengbin Ding and Yuwan Hong and Fan Wu and Peigen Zhang and Yizhe Guo and Ziyu Liu and Tian-Ling Ren and He Tian},
year = {2026},
journal = {Nature Communications},
doi = {10.1038/s41467-026-77789-2},
url = {https://doi.org/10.1038/s41467-026-77789-2}
}RIS
TY - JOUR TI - Stress-engineered 0.34 nm graphene memristors for low-power probabilistic computing AU - Yanming Liu AU - Jinzhu Li AU - Zhoujie Pan AU - Hengbin Ding AU - Yuwan Hong AU - Fan Wu AU - Peigen Zhang AU - Yizhe Guo AU - Ziyu Liu AU - Tian-Ling Ren AU - He Tian PY - 2026 JO - Nature Communications DO - 10.1038/s41467-026-77789-2 UR - https://doi.org/10.1038/s41467-026-77789-2 ER -
APA
Liu, Y., Li, J., Pan, Z., Ding, H., Hong, Y., Wu, F., Zhang, P., Guo, Y., Liu, Z., Ren, T., & Tian, H. (2026). Stress-engineered 0.34 nm graphene memristors for low-power probabilistic computing. Nature Communications. https://doi.org/10.1038/s41467-026-77789-2
Source records
- crossref · retrieved 2026-11-08T13:31:26.987Z