Organic Photonic Synapses for Application of Bioinspired Neuromorphic Visual Perception and Beyond
Abstract The acquisition and retention of visual information in biological systems are governed by the dynamic regulation of synaptic weights within visual neural circuits. By emulating this process, light‐responsive synaptic devices have emerged as pivotal components for the development of low‐power artificial vision systems and next‐generation neuromorphic computing architectures. Among these, organic photonic synapses (OPSs) offer unique advantages, including mechanical flexibility, low energy consumption, and tunable optoelectronic properties, positioning them as promising candidates for bioinspired neuromorphic vision platforms. Drawing inspiration from the hierarchical processing of natural vision, OPS‐based devices enable direct sensing and preprocessing of light stimuli at the hardware level, facilitating efficient in‐sensor neuromorphic computing for advanced visual cognitive tasks. In this review, a comprehensive overview of recent advances in OPSs is provided, encompassing diverse material systems, device architectures, operational mechanisms, and applications in bioinspired visual perception. Moreover, the integration of OPSs with bioelectronic platforms, in conjunction with flexible electronics is delved into, thereby opening new avenues for advanced applications in wearable visual enhancement, insect robotics, and neuroprosthetic devices. Finally, the current challenges and future perspectives are discussed toward the development of high‐performance, scalable, and multifunctional neuromorphic visual systems based on OPSs.
Authors
- Jiahui Ding (ORCID: https://orcid.org/0009-0002-1607-2888)
- Shuanglong Wang (ORCID: https://orcid.org/0000-0002-7016-791X)
- Jiepei Cao (ORCID: https://orcid.org/0000-0002-3459-202X)
- Qingchen Dong (ORCID: https://orcid.org/0000-0003-4363-2748)
- Hong Lian
- Shihui Dong
- Gong Yau Lan
- Zhitao Qin
Institutions
- Shanghai University (CN)
- Chinese University of Hong Kong (HK)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-19
- DOI
- https://doi.org/10.1002/adfm.202522513
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00