Authors
Zirui Wang, Bohao Song, Songqiao Li, Zechen Liang, Qingyu Wang, Dandan Zhang, Jiangpeng Li, Jingpeng Wu, Xin Wang, Guanghao Lu
Published in
Advanced materials (Deerfield Beach, Fla.). Pages e75044. Sep 17, 2026. Epub Sep 17, 2026.
Abstract
Neuromorphic visual systems are core technologies enabling round-the-clock perception for the Internet of Things (IoT). While reliable sensing in complex lighting conditions requires robust near-infrared (NIR) capabilities, existing optoelectronic devices struggle to simultaneously achieve a broad NIR spectral response and high electronic performance. This limitation severely hinders their practical applications in anti-interference imaging and intelligent recognition. Here, high-performance organic photonic synaptic transistors (OPSTs) are reported, which employ a C8-BTBT channel layer and a perylene-TCNQ cocrystal NIR photosensitive layer, with polystyrene (PS) incorporated to enable efficient interfacial charge modulation and assist charge transport. The OPSTs exhibit a high mobility of 2.65 cm2·V-1·s-1 and an on/off ratio exceeding 106, while extending the spectral response range to the NIR region up to 1200 nm, breaking the inherent trade-off between spectral response bandwidth and charge mobility that limits most existing NIR optoelectronic synapses. Benefiting from its excellent NIR response and electrical robustness, the device successfully emulates a series of retinal-like optical synapse plasticity behaviors. It achieves high-contrast imaging in simulated scattering media and attains a 94% face recognition accuracy in neural network simulations. This work offers a promising strategy for anti-interference NIR neuromorphic vision in complex illumination and all-weather IoT applications.
PMID:
42755249
Bibliographic data and abstract were imported from PubMed on 18 Sep 2026.
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