Integrated near-infrared spectral sensing

Kaylee D. Hakkel (), Maurangelo Petruzzella, Fang Ou, Anne Klinken, Francesco Pagliano, Tianran Liu, Rene P. J. Veldhoven and Andrea Fiore
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Kaylee D. Hakkel: Eindhoven University of Technology
Maurangelo Petruzzella: Eindhoven University of Technology
Fang Ou: Eindhoven University of Technology
Anne Klinken: Eindhoven University of Technology
Francesco Pagliano: Eindhoven University of Technology
Tianran Liu: Eindhoven University of Technology
Rene P. J. Veldhoven: Eindhoven University of Technology
Andrea Fiore: Eindhoven University of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Spectral sensing is increasingly used in applications ranging from industrial process monitoring to agriculture. Sensing is usually performed by measuring reflected or transmitted light with a spectrometer and processing the resulting spectra. However, realizing compact and mass-manufacturable spectrometers is a major challenge, particularly in the infrared spectral region where chemical information is most prominent. Here we propose a different approach to spectral sensing which dramatically simplifies the requirements on the hardware and allows the monolithic integration of the sensors. We use an array of resonant-cavity-enhanced photodetectors, each featuring a distinct spectral response in the 850-1700 nm wavelength range. We show that prediction models can be built directly using the responses of the photodetectors, despite the presence of multiple broad peaks, releasing the need for spectral reconstruction. The large etendue and responsivity allow us to demonstrate the application of an integrated near-infrared spectral sensor in relevant problems, namely milk and plastic sensing. Our results open the way to spectral sensors with minimal size, cost and complexity for industrial and consumer applications.

Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1038/s41467-021-27662-1

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