CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging

Simultaneously capturing static images and processing dynamic visual information within a single sensor enables a more comprehensive and efficient acquisition of scene information, thereby enhancing the understanding and processing of complex scenes. However, current artificial visual systems present significant challenges in device integration and multimodal operation. Here, we developed a 640×512-pixel CMOS-integrated organic neuromorphic imager featuring dual modes: standard (frame-based imaging) and synaptic (neuromorphic imaging). In synaptic mode, the system extracts high-resolution spatiotemporal maps (light distribution and motion trajectories) from final frames, decoding temporal sequences of light events through contrast analysis. The neuromorphic device demonstrates adjustable memory behavior through modulation of charge recombination-trapping dynamics, enabling multi-level memory functionality. We further developed a CMOS-compatible photolithography method, which supports high-resolution and non-destructive patterning of organic neuromorphic devices. The fabricated imager allows in-sensor memorization (>18 min) and real-world spatiotemporal imaging with reduced computation resource, demonstrating its potential for industrial monitoring and motion detection.

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Publication Details

Journal
Nature Communications
Published
2025-05-08
DOI
https://doi.org/10.1038/s41467-025-59446-2
Citations
11
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
8.03

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article

CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging

Ge Mu, Xin Tang, Kangkang Weng, Xiaolong Zheng et al.
11 citations
Nature Communications
Advanced Memory and Neural Computing
8.03
article

CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging

Ge Mu, Xin Tang, Kangkang Weng, Xiaolong Zheng, Qun Hao, Kai‐Mei C. Fu, Yanfei Liu, Yimei Tan, Chenxi Liu, Youzhi Shi, Salihuojia Talanti
article en
11 citations

Abstract

Simultaneously capturing static images and processing dynamic visual information within a single sensor enables a more comprehensive and efficient acquisition of scene information, thereby enhancing the understanding and processing of complex scenes. However, current artificial visual systems present significant challenges in device integration and multimodal operation. Here, we developed a 640×512-pixel CMOS-integrated organic neuromorphic imager featuring dual modes: standard (frame-based imaging) and synaptic (neuromorphic imaging). In synaptic mode, the system extracts high-resolution spatiotemporal maps (light distribution and motion trajectories) from final frames, decoding temporal sequences of light events through contrast analysis. The neuromorphic device demonstrates adjustable memory behavior through modulation of charge recombination-trapping dynamics, enabling multi-level memory functionality. We further developed a CMOS-compatible photolithography method, which supports high-resolution and non-destructive patterning of organic neuromorphic devices. The fabricated imager allows in-sensor memorization (>18 min) and real-world spatiotemporal imaging with reduced computation resource, demonstrating its potential for industrial monitoring and motion detection.

Nature CommunicationsVol. 16(1)
Changchun University of Science and Technology (CN), Beijing Institute of Technology (CN), Xintai People's Hospital (CN), OriginWater (China) (CN), Tang Hospital (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Advanced Memory and Neural Computing
8.03
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