Environment‐Adaptive Neuromorphic Transistor Enabled by Background‐Light‐Driven Response Reconfiguration
ABSTRACT Organic optoelectronic synaptic devices have attracted considerable attention for applications in neuromorphic vision systems. However, most reported devices operate with fixed response modes, which limits their adaptability to complex and dynamic lighting environments and thereby restricts their applications in dynamic visual information processing and environmental adaptation. In particular, background light is typically regarded only as an external environmental factor rather than as a key variable for modulating device response modes. Here, an ambient‐light‐modulated biomimetic synaptic transistor incorporating Au nanoparticles into the semiconductor layer is reported. The device exhibits environment‐dependent switching between excitatory and inhibitory photocurrent responses to the same optical signal under different background light conditions, thus producing reconfigurable photoresponse behavior. Leveraging this bidirectional photoresponse, the device achieves reliable color and digit recognition under variable ambient illumination. This background‐light‐mediated reconfiguration of synaptic behavior enables dynamic reconstruction of visual perception and behavioral decision‐making, while mimicking environmentally adaptive visual behaviors observed in biological systems. Moreover, through circuit design and system‐level integration, a closed‐loop pathway of “environmental adaptation–response adjustment–behavioral decision reconstruction” is established. This work provides a practical strategy for developing optoelectronic neuromorphic devices with high environmental adaptability.
Authors
- Zirui Zhou (ORCID: https://orcid.org/0000-0001-6740-2432)
- Hongbo B. Lu (ORCID: https://orcid.org/0000-0001-8034-3035)
- Xiaohong Wang (ORCID: https://orcid.org/0000-0002-7289-8521)
- Longzhen Qiu (ORCID: https://orcid.org/0000-0002-8356-6303)
- Xiaocheng Wu
- Zixiang Lu
- Wei Xia
Institutions
- Hefei University of Technology (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1002/adfm.78995
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China