Hyperspectral quantum-dot image sensors via in-pixel reconfigurable band-alignment

Ge Mu; Cheng Bi; Jintao Zou; Yanfei Liu; Qun Hao; Xin Tang

doi:10.1038/s41566-026-01860-z

Hyperspectral quantum-dot image sensors via in-pixel reconfigurable band-alignment

Ge Mu
, Cheng Bi
, Jintao Zou
, Yanfei Liu
, Qun Hao
, Xin Tang^*

^*此作品的通讯作者

Beijing Institute of Technology
Minzu University of China
Ltd.
Changchun University of Science and Technology

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

High-resolution short-wave infrared hyperspectral imaging enables non-destructive material identification and imaging through scattering media, paving the way for transformative applications in portable diagnostics, precision agriculture, environmental monitoring and space exploration. However, conventional hyperspectral imagers face a compromise between spatial resolution, spectral resolution and device footprint. Here we report a miniaturized hyperspectral image sensor that mitigates this trade-off by leveraging monolithically integrated, bias-reconfigurable stacked colloidal quantum dot junctions and a bias-programmable spectral reconstruction algorithm. By applying a defined sequence of single-polarity increasing bias voltages, the interfacial band alignment can be tuned, thus mediating the collection of photon-generated carriers in colloidal quantum dot layers with different energy gap. Our imager achieves spatial resolution of 1,280 × 1,024, spectral resolution of 1 nm, reconstruction accuracy of 0.055 nm, peak detectivity above 10¹³ jones and broadband coverage (400–1,700 nm), all within a compact pixel footprint of 15 × 15 µm². The high signal-to-noise ratio and spatial resolution result in accurate reconstruction of hyperspectral image information, enabling food quality monitoring, chemical solvents discrimination and materials identification.

源语言	英语
页（从-至）	523-531
页数	9
期刊	Nature Photonics
卷	20
期	5
DOI	https://doi.org/10.1038/s41566-026-01860-z
出版状态	已出版 - 5月 2026
已对外发布	是

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title = "Hyperspectral quantum-dot image sensors via in-pixel reconfigurable band-alignment",

abstract = "High-resolution short-wave infrared hyperspectral imaging enables non-destructive material identification and imaging through scattering media, paving the way for transformative applications in portable diagnostics, precision agriculture, environmental monitoring and space exploration. However, conventional hyperspectral imagers face a compromise between spatial resolution, spectral resolution and device footprint. Here we report a miniaturized hyperspectral image sensor that mitigates this trade-off by leveraging monolithically integrated, bias-reconfigurable stacked colloidal quantum dot junctions and a bias-programmable spectral reconstruction algorithm. By applying a defined sequence of single-polarity increasing bias voltages, the interfacial band alignment can be tuned, thus mediating the collection of photon-generated carriers in colloidal quantum dot layers with different energy gap. Our imager achieves spatial resolution of 1,280 {\texttimes} 1,024, spectral resolution of 1 nm, reconstruction accuracy of 0.055 nm, peak detectivity above 10¹³ jones and broadband coverage (400{\textendash}1,700 nm), all within a compact pixel footprint of 15 {\texttimes} 15 µm². The high signal-to-noise ratio and spatial resolution result in accurate reconstruction of hyperspectral image information, enabling food quality monitoring, chemical solvents discrimination and materials identification.",

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AU - Mu, Ge

AU - Bi, Cheng

AU - Zou, Jintao

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AU - Tang, Xin

PY - 2026/5

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N2 - High-resolution short-wave infrared hyperspectral imaging enables non-destructive material identification and imaging through scattering media, paving the way for transformative applications in portable diagnostics, precision agriculture, environmental monitoring and space exploration. However, conventional hyperspectral imagers face a compromise between spatial resolution, spectral resolution and device footprint. Here we report a miniaturized hyperspectral image sensor that mitigates this trade-off by leveraging monolithically integrated, bias-reconfigurable stacked colloidal quantum dot junctions and a bias-programmable spectral reconstruction algorithm. By applying a defined sequence of single-polarity increasing bias voltages, the interfacial band alignment can be tuned, thus mediating the collection of photon-generated carriers in colloidal quantum dot layers with different energy gap. Our imager achieves spatial resolution of 1,280 × 1,024, spectral resolution of 1 nm, reconstruction accuracy of 0.055 nm, peak detectivity above 10¹³ jones and broadband coverage (400–1,700 nm), all within a compact pixel footprint of 15 × 15 µm². The high signal-to-noise ratio and spatial resolution result in accurate reconstruction of hyperspectral image information, enabling food quality monitoring, chemical solvents discrimination and materials identification.

AB - High-resolution short-wave infrared hyperspectral imaging enables non-destructive material identification and imaging through scattering media, paving the way for transformative applications in portable diagnostics, precision agriculture, environmental monitoring and space exploration. However, conventional hyperspectral imagers face a compromise between spatial resolution, spectral resolution and device footprint. Here we report a miniaturized hyperspectral image sensor that mitigates this trade-off by leveraging monolithically integrated, bias-reconfigurable stacked colloidal quantum dot junctions and a bias-programmable spectral reconstruction algorithm. By applying a defined sequence of single-polarity increasing bias voltages, the interfacial band alignment can be tuned, thus mediating the collection of photon-generated carriers in colloidal quantum dot layers with different energy gap. Our imager achieves spatial resolution of 1,280 × 1,024, spectral resolution of 1 nm, reconstruction accuracy of 0.055 nm, peak detectivity above 10¹³ jones and broadband coverage (400–1,700 nm), all within a compact pixel footprint of 15 × 15 µm². The high signal-to-noise ratio and spatial resolution result in accurate reconstruction of hyperspectral image information, enabling food quality monitoring, chemical solvents discrimination and materials identification.

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Hyperspectral quantum-dot image sensors via in-pixel reconfigurable band-alignment

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