4.8 Article

Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays

Journal

NANO RESEARCH
Volume -, Issue -, Pages -

Publisher

TSINGHUA UNIV PRESS
DOI: 10.1007/s12274-023-5669-z

Keywords

infrared plasmonics; germanium (Ge); aluminum (Al); thermal optics; longwave infrared (LWIR) multispectral system

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A multispectral camera records image data in various wavelengths to capture additional information. While multispectral imaging in visible wavelengths has gained popularity, it is still an emerging area in longwave infrared (LWIR) due to material and technology limitations. LWIR multispectral cameras can capture emission spectra for various applications. In this work, we demonstrate an LWIR multispectral image sensor using an aluminum-based plasmonic filter array in germanium. The prototype device is calibrated and the multispectral images are reconstructed using deep learning methods.
A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and color filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR, 8-14 mu m) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum (Al)-based plasmonic filter array sandwiched in germanium (Ge). To realize the multispectral sensor, the filter arrays are then integrated into a three-dimensional (3D) printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-theart deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.

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