Polarity‐Programmable Response in Bipolar‐Barrier Heterojunctions for Reconfigurable Detection
ABSTRACT Next‐generation intelligent vision systems demand not only high‐fidelity sensing but also in‐sensor processing that is fast, adaptive, and energy‐efficient. Existing in‐sensor paradigms ignore photocurrent polarity—an intrinsic decision variable—forcing computation into amplitude thresholds or multi‐device pipelines. Here, we present a polarity‐programmable optoelectronic device based on bipolar‐barrier heterostructures equipped separately with vertical and horizontal channels. By exploiting the interplay between bipolar barriers, the device enables modulation of photocurrent polarity—realizing dynamic ternary‐state output (‘−1’, ‘0’, ‘+1’) governed solely by laser position, bias voltage, wavelength, and power‐ coded. The vertical structure exhibits a bipolar response with an ultralow switching bias of −0.025 V (less than 1/10 that of the horizontal structure), along with wavelength‐tunable polarity, a responsivity of 0.22 A/W, a detectivity of 8.6 × 10 9 cm Hz 1/2 W −1 , and a fast response time of ∼ 121 µs due to its large photosensitive area, shortened carrier transport, and wavelength‐dependent optical field distribution. Programming wavelength and power density enable a single device to deterministically realize all Boolean gates, polarity‐resolved imaging, and visible‐infrared dual‐channel transmission. This multidimensional polarity‐programmable pathway in bipolar‐barrier heterojunctions intrinsically integrates sensing, signal encoding, and logic operations within a single physical unit, establishing polarity programming as a scalable paradigm for optoelectronic computing.
Authors
- Zehao Liu (ORCID: https://orcid.org/0009-0008-2509-1099)
- X H Chen
- Fengyi Zhu (ORCID: https://orcid.org/0000-0001-7110-1165)
- Xuhao Fan (ORCID: https://orcid.org/0000-0002-6148-0883)
- 茹文君
- Haibo Shu (ORCID: https://orcid.org/0000-0003-1728-2190)
- Changlong Liu (ORCID: https://orcid.org/0000-0002-6943-1090)
- Changyi Pan (ORCID: https://orcid.org/0000-0001-7700-3216)
- G Li
- Xi Zhang (ORCID: https://orcid.org/0000-0002-4638-3997)
- Sheng Ni
- Songyuan Ding
- Yi Zhou
- Chaojie Xia
- Yuhang Ma
- Chongyu Li
- Xiaohan Wei
- Shian Mi
Institutions
- Northwestern Polytechnical University (CN)
- Shanghai Institute of Technical Physics (CN)
- Institute for Advanced Study (DE)
- China Jiliang University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-04-26
- DOI
- https://doi.org/10.1002/adfm.75623
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province