2D Time‐Stretching Anisotropic Synapse Realizing In‐Sensor Intensity‐Spanning Visual Feature Fusion

D Decai Ouyang (State Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan P. R. China) M Mengqi Wang N Na Zhang (High Magnetic Field Laboratory, Hefei Institutes of Physical Science) W Wenke He (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China 1 , Chengdu 611731,) D Da Huo Y Yuan Li T Tianyou Zhai (State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering)

Abstract

AbstractHigh‐dynamic‐range (HDR) visual environments, where extremely bright and dark regions coexist, pose major challenges for conventional imaging systems that rely on multi‐frame exposure fusion and cloud‐based post‐processing. These approaches often suffer from high latency, limited efficiency, and privacy concerns, making them unsuitable for real‐time or edge‐level intelligent vision. Here, a 2D Time‐Stretching Anisotropic Synapse (2D TSAS) is reported that enables in‐sensor intensity‐spanning feature fusion from a single image frame. The 2D TSAS uniquely integrates two key features of NbOI2 material: in‐plane anisotropy, which gives rise to polarization‐resolved optical responses, and a time‐stretching photoresponse arising from multi‐channel transition‐relaxation. This dual‐mode mechanism enables direct encoding and temporal integration of spatial‐polarization and luminance features during photoexcitation. Leveraging this behavior, a neuromorphic preprocessing strategy is constructed for single‐shot visual learning across extreme brightness domains. The system achieves accelerated model convergence with minimal training loss, reaching recognition accuracies of ≈95.41% on NWPU‐RESISC45 and ≈95.39% on MNIST. This work offers a compact and efficient solution for contrast‐adaptive intelligent vision in complex real‐world environments.

Article Details

Volume / Issue Vol. 37, Issue 33
Published August 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (7)

D

Decai Ouyang

State Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan P. R. China

M

Mengqi Wang

N

Na Zhang

High Magnetic Field Laboratory, Hefei Institutes of Physical Science

W

Wenke He

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China 1 , Chengdu 611731,

D

Da Huo

Y

Yuan Li

T

Tianyou Zhai

State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering