Dual‐Functional Optoelectronic Devices Composed of Cs <sub>3</sub> Cu <sub>2</sub> I <sub>5</sub> /Graphene/Ge Multi‐Heterostructures Enabling Deep‐Ultraviolet Synaptic Behavior and Near‐Infrared Photodetection
Multi-functional optoelectronic devices that integrate the capacities of neuromorphic processing and photodetection are of pivotal importance for advancing optoelectronic techniques. Here, we present a dual-functional optoelectronic device based on a multi-heterostructure of Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub>/graphene/Ge, which realizes high-performance deep-ultraviolet (DUV) synaptic behavior and self-driven near-infrared (NIR) photodetection simultaneously. On one hand, the device leverages the photogating effect in the Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub>/graphene heterostructure to produce persistent photo-conductivity, emulating synaptic behavior upon DUV light stimulation. Electron trapping by introducing B<sub>4</sub>PyMPM as capture sites significantly promotes spatial divorcement of photocarriers, guaranteeing remarkably improved synaptic characteristics. The utilizations in DUV fingerprint recognition with an accuracy of 97.8%, and optoelectronic reservoir computing with recognition accuracy reaching 100% for three 4-bit numbers are demonstrated. On the other hand, the device utilizes the photovoltaic effect in the graphene/Ge heterostructure to generate photoresponse at zero working bias, enabling self-driven photodetection upon NIR light illumination. The wide-bandgap Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> functions as an effective NIR anti-reflection layer to induce a strong light trapping effect, giving rise to boosted photoresponse properties. The excellent photoresponse supports the device in performing single-pixel NIR optical imaging. It is believed that this work is inspiring to the design of high-performance multi-functional optoelectronic devices toward integrated and intelligence applications.
Authors
- Liangpan Yang
- Yu Wu (ORCID: https://orcid.org/0000-0003-0139-8105)
- Chao Xie (ORCID: https://orcid.org/0000-0002-3042-8409)
- Can Fu (ORCID: https://orcid.org/0000-0002-8361-0722)
- Wenhua Yang (ORCID: https://orcid.org/0000-0003-0108-6785)
- Shunzi Li
- Yuhang Shan
- Deng Ke
Institutions
- Anhui University (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2026-01-22
- DOI
- https://doi.org/10.1002/adma.202522683
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 21.93