Day–Night Visual Perception Enabled by Vis–NIR Broadband Adaptive Transistors
Broadband adaptive vision is pivotal in enabling robust day-night visual operation in applications like autonomous driving and smart security. However, most existing active perception systems are confined to the visible spectrum regime. Here, we report a Vis-NIR broadband organic active adaptation transistor (VN-OAAT) by incorporating a dielectric embedded with ternary bulk-heterojunction, which simultaneously broadens the spectral response (400-1200 nm) and tunes the charge trapping activation energy. Through precise donor-acceptor compositional engineering, the device exhibits light-intensity-dependent photoresponse that enables photopic adaptation under high-illumination conditions and efficient photodetection in low-light environments, each spanning four orders of magnitude in intensity. Imaging experiments and simulations demonstrate that the device ensures all-day accurate vision with recognition accuracy over 96%. This ternary-heterojunction strategy addresses the intrinsic mismatch between high-performance NIR semiconductors and OAAT, establishing an efficient platform for broadband adaptive sensing in day-night machine vision applications.
Authors
- Chong‐an Di (ORCID: https://orcid.org/0000-0002-6183-1321)
- Daoben Zhu (ORCID: https://orcid.org/0000-0002-6354-940X)
- Xiaozhang Zhu (ORCID: https://orcid.org/0000-0002-6812-0856)
- Yongjie Chen (ORCID: https://orcid.org/0000-0003-0853-9767)
- Chengyu Zhang (ORCID: https://orcid.org/0000-0001-7257-9689)
- Yutao Ge
- Ye Zou (ORCID: https://orcid.org/0000-0002-7642-0226)
- Zepang Zhan
- Yunjiang Zi
- Wei Wang
- Zixuan Liu
- Weijie Wang
- Liyao Liu
- Xiaojuan Dai
- Zitong Liu
Institutions
- Shanghai Jiao Tong University (CN)
- Chinese Academy of Sciences (CN)
- Beijing National Laboratory for Molecular Sciences (CN)
- Ministry of Education (BD)
- University of Chinese Academy of Sciences (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2026-07-28
- DOI
- https://doi.org/10.1002/adma.74310
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00