11-bit two-dimensional floating-gate memories
Floating-gate memories (FGMs) show great promise for neuromorphic computing in efficient data-centric applications. However, their limited single-device state capacity remains insufficient for highly integrated precision computing. Here, we demonstrate 11-bit two-dimensional (2D) MoS<sub>2</sub> FGMs by contacting the 2D channels with bismuth electrodes, enabling 100 μA on-state current with 10<sup>8</sup> on/off ratio and reducing the current noise by 3 times (approaching the equipment limits) due to the Schottky barrier-free interfaces. Moreover, we employed a dual-pulse state editing scheme enhancing the stability of our FGMs. The devices show as high as 2,249 distinct conductance levels (>11-bit) while maintaining 230 ns operation speed, >10<sup>4 </sup>s retention, and >10<sup>5</sup> cycle endurance. Furthermore, the gate-injection operation prevents the influence from generated defects during cycling, maintaining low noise even after 10<sup>5</sup> cycles and at 85 °C. Theoretical analysis reveals interfacial defects as the primary state-number limitation, suggesting 17-bit capacity is achievable through further trap density reduction. This work establishes 2D FGMs as promising candidates for high-bit-density, low-power neuromorphic hardware.
Authors
- Ruohao Hong
- Yuchen Cai
- Yanrong Wang (ORCID: https://orcid.org/0000-0002-2277-9671)
- Baoxing Zhai
- Tao Yan (ORCID: https://orcid.org/0000-0001-8066-2124)
- Kai Xu (ORCID: https://orcid.org/0000-0001-7792-8235)
- Jun He (ORCID: https://orcid.org/0000-0002-5998-5225)
- Shu‐Hui Li (ORCID: https://orcid.org/0000-0003-0141-8743)
- Zhenxing Wang (ORCID: https://orcid.org/0000-0002-9882-4774)
- Feng Wang (ORCID: https://orcid.org/0000-0001-7428-2336)
- Ming-Yang Cao
- Xueying Zhan
Institutions
- Wuhan University (CN)
- Henan Academy of Sciences (CN)
- Zhejiang Lab (CN)
- National Center for Nanoscience and Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-10-20
- DOI
- https://doi.org/10.1038/s41467-025-64333-x
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- National Natural Science Foundation of China
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Key Laboratory of Nanosystem and Hierarchical Fabrication, Chinese Academy of Sciences
- Youth Innovation Promotion Association