CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging
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 syste...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59446-2 |
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| author | Salihuojia Talanti Kerui Fu Xiaolong Zheng Youzhi Shi Yimei Tan Chenxi Liu Yanfei Liu Ge Mu Qun Hao Kangkang Weng Xin Tang |
| author_facet | Salihuojia Talanti Kerui Fu Xiaolong Zheng Youzhi Shi Yimei Tan Chenxi Liu Yanfei Liu Ge Mu Qun Hao Kangkang Weng Xin Tang |
| author_sort | Salihuojia Talanti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98cbae443eca40e08b780d18b2a9149e |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-98cbae443eca40e08b780d18b2a9149e2025-08-20T01:49:37ZengNature PortfolioNature Communications2041-17232025-05-011611910.1038/s41467-025-59446-2CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imagingSalihuojia Talanti0Kerui Fu1Xiaolong Zheng2Youzhi Shi3Yimei Tan4Chenxi Liu5Yanfei Liu6Ge Mu7Qun Hao8Kangkang Weng9Xin Tang10School of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologyXinIR Technology (Beijing) Co., LTDSchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologySchool of Optics and Photonics, Beijing Institute of TechnologyAbstract 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.https://doi.org/10.1038/s41467-025-59446-2 |
| spellingShingle | Salihuojia Talanti Kerui Fu Xiaolong Zheng Youzhi Shi Yimei Tan Chenxi Liu Yanfei Liu Ge Mu Qun Hao Kangkang Weng Xin Tang CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging Nature Communications |
| title | CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging |
| title_full | CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging |
| title_fullStr | CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging |
| title_full_unstemmed | CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging |
| title_short | CMOS-integrated organic neuromorphic imagers for high-resolution dual-modal imaging |
| title_sort | cmos integrated organic neuromorphic imagers for high resolution dual modal imaging |
| url | https://doi.org/10.1038/s41467-025-59446-2 |
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