Recent advances in flexible optoelectronic synapses: Mechanisms, materials, and neuromorphic applications
Abstract Traditional von Neumann architectures face pronounced memory‐wall and energy‐consumption bottlenecks when processing massive unstructured visual data. Inspired by biological visual systems, optoelectronic synapses, which inherently integrate sensing, computing, and memory in mechanically compliant devices, are emerged as the most promising building blocks for neuromorphic hardware. Especially, flexible optoelectronic synapses could enable soft, wearable, and bio‐integrated neuromorphic vision systems that sense, process, and remember optical information directly on curved or moving surfaces, owing to their low‐power in‐sensor visual intelligence. This review starts from the fundamental mechanisms that govern neuromorphic behavior in optoelectronic synapse, and then summarizes the recent progress in flexible optoelectronic synapses based on diverse material platforms. Despite rapid progress, the instability in optoelectronic synapse keeps hindering its further development. We carefully examine this bottleneck and outline the potential solutions. More importantly, we highlight the disruptive potential of flexible optoelectronic synapses in closed‐loop biohybrid systems and next‐generation visual brain–computer interfaces.
Authors
- Meicheng Li (ORCID: https://orcid.org/0000-0002-0731-741X)
- Yuyi Feng (ORCID: https://orcid.org/0000-0003-2761-1832)
- Caofeng Pan (ORCID: https://orcid.org/0000-0001-6327-9692)
- Yingjie Li (ORCID: https://orcid.org/0000-0002-8022-2871)
- Rui Su (ORCID: https://orcid.org/0000-0002-8861-1222)
- Wenqiang Yang (ORCID: https://orcid.org/0000-0002-3188-238X)
- Da-Quan Yang (ORCID: https://orcid.org/0000-0002-0187-6739)
Institutions
- North Carolina State University (US)
- Beijing University of Posts and Telecommunications (CN)
- North China Electric Power University (CN)
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (CN)
- Beihang University (CN)
Publication Details
- Journal
- FlexMat.
- Published
- 2026-11-02
- DOI
- https://doi.org/10.1002/flm2.70153
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00