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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年3月12日

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

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.