多光谱图像
计算机科学
多光谱模式识别
人工智能
计算机视觉
瓶颈
图像传感器
特征提取
模式识别(心理学)
卷积(计算机科学)
特征(语言学)
遥感
感知
作者
Shi Fang,Fakun Wang,Fengqi Cui,Jinyang Huang,Wenduo Chen,Chao Han,Hui Ma,Jianbo Yu,Zhenhan Zhang,Chongwu Wang,Qijie Wang
标识
DOI:10.1038/s41467-026-77260-2
摘要
Multispectral features across visible (Vis), near-infrared (NIR) and mid-infrared (MIR) provide complementary information for intelligent vision in challenging environments. Precise extraction of multispectral features within the sensor level becomes increasingly critical, yet remains elusive for next-generation vision systems. Here we report a multispectral sensor constructed by a MoTe2/BP heterojunction with gate-tunable band alignments between type-Ⅰ and type-Ⅱ, enabling bipolar Vis/NIR (473 ~ 1064 nm) and unipolar MIR (3.7 and 4.6 μm) responses under zero bias for in-sensor Vis/NIR convolution and MIR target recognition. Beyond single-device operation, a 9×1 linear array validates device uniformity and preliminary array-level feasibility. Furthermore, the multispectral sensor extracts crosstalk-free Vis/NIR and MIR features under co-illumination of 980-nm and 3.7-μm light, increasing recognition accuracy from 28% to 84% in reconstructed system-level demonstrations. By fully leveraging the tunable 2D-material band structure, this work demonstrates a universal sensor configuration for multispectral perception with in-sensor processing functions. Achieving precise extraction of multispectral features is a critical bottleneck for next-generation vision systems. Fang et al. report a multispectral sensor that distinguishes input light in the visible/near-infrared ranges from that in the mid-infrared range at the sensor level, enabling in-sensor multispectral processing.
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