Chelated tin halide perovskite for near-infrared neuromorphic imaging array enabling object recognition and motion perception

Tianhua Liu, Ziquan Yuan, Lixia Wang, Cong Shan, Qinglin Zhang, Hao Chen, Hao Wang, Weitong Wu, Le Huang, Yang Chai () and Xiangyue Meng ()
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Tianhua Liu: University of Chinese Academy of Sciences
Ziquan Yuan: University of Chinese Academy of Sciences
Lixia Wang: University of Chinese Academy of Sciences
Cong Shan: University of Chinese Academy of Sciences
Qinglin Zhang: Guangdong University of Technology
Hao Chen: University of Chinese Academy of Sciences
Hao Wang: University of Chinese Academy of Sciences
Weitong Wu: University of Chinese Academy of Sciences
Le Huang: Guangdong University of Technology
Yang Chai: The Hong Kong Polytechnic University
Xiangyue Meng: University of Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Neuromorphic imaging arrays integrate sensing, memory, and processing for efficient spatiotemporal fusion, enabling intelligent object and motion recognition in autonomous and surveillance systems. Halide perovskites offer potential for neuromorphic imaging by regulating photogenerated ions and charges, but lead toxicity and limited response range remain key limitations. Here, we present lead-free non-toxic formamidinium tin triiodide perovskites functionalized with bio-friendly quercetin molecules via a multi-site chelate strategy, achieving favorable near-infrared response and optoelectronic properties. Leveraging a non-equilibrium photogenerated carrier strategy, the formamidinium tin triiodide-quercetin based near-infrared optoelectronic synapses exhibit key synaptic features for practical applications, including quasi-linear time-dependent photocurrent generation, prolonged photocurrent decay, high stability, and low energy consumption. Ultimately, a 12 × 12 real-time neuromorphic near-infrared imaging array is constructed on thin-film transistor backplanes, enabling hardware-level spatiotemporal fusion for robust object recognition and motion perception in complex environments for autonomous and surveillance systems.

Date: 2025
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DOI: 10.1038/s41467-025-59624-2

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