We demonstrate a novel, fully-integrated approach to spectral sensing in the near-infrared range suitable for analyzing the chemical composition of organic materials. The sensor consists of 16 detector pixels, each forming a resonant-cavity enhanced photodetector consisting of an InGaAs/InP photodiode and a tuning layer enclosed in a planar cavity formed by two metal mirrors. For wavelengths meeting the resonance condition of the optical cavity, the absorption in the photodiode is enhanced, which leads to a wavelength-specific response of the photodetector. As the thickness of the tuning layer is varied throughout the pixels, each of the 16 photodetectors features an individual complex spectral response with several peaks of about 50 nm linewidth and responsivity above 0.1 A/W. All pixels together cover the whole wavelength range from 900 nm to 1700 nm, allowing for the analysis of broad spectral features typical for diffuse reflectance spectra of organic materials in the near-infrared range. The photocurrents read-out from the spectral sensors can be combined with chemometric analysis methods to determine the material composition. We demonstrate the performance of the spectral sensor for the determinate of moisture in rice grains, leading to a coefficient of determination of R² = 0.97. Other demonstrated applications include the quantification of the sugar content in tomatoes, fat and protein content in raw cow milk and the classification of different types of plastic. With a size of 1.5 mm by 1.5mm and a fabrication scheme based on optical lithography, this on-chip spectral sensor yields potential for large-scale production. Together with the mechanical stability of the sensor, this approach is an important step towards portable, low-cost spectral sensing solutions.
We demonstrate a near-infrared (900-1650nm) spectral sensor based on an array of 16 pixels for classifying and quantifying materials and their composition. These pixels consist of resonant-cavity enhanced photodetectors containing a thin absorbing layer, tuning element and cavity. Using a wafer-scale optical lithography process, we achieve a tunable, wavelength-specific response with narrow linewidths of 50nm and high responsivity (R>0.1A/W). The customizability of the response, small-size and robustness make it suitable for portable spectroscopic solutions in a wide variety of applications. The sensing performance is demonstrated on the prediction of moisture in rice with a coefficient of determination of R^2=0.95.
KEYWORDS: Sensors, Near infrared, Metals, Chemical analysis, Biological and chemical sensing, Wafer bonding, Visible radiation, Tissues, Spectral resolution, Silicon
Spectral sensing in the near-infrared range is of increasing interest for application areas ranging from industrial processes to the agri-food sector, as it allows measuring the chemical composition of organic materials in a fast and non-destructive manner. On-field applications demand spectral sensors with a large spectral range, low angular dependence, robust geometry and small footprint, while being manufacturable on large scale at low cost. To meet these requirements, we developed a multi-pixel array of resonant-cavity enhanced detectors using an InP-membrane-on-silicon platform, where each of the 16 pixels provides an individual photoresponse with a number of resonances, which together cover the wavelength range of 800-1700 nm. The pixels consist of two metal mirrors, which enclose an InP p-i-n photodiode with an InGaAs absorber layer and a tuning layer, which varies in thickness to create the individual photoresponses of different pixels. The multi-pixel arrays are fabricated by adhesive wafer bonding followed by a series of lithography steps to define the tuned pixels and metal contacts for the photocurrent read-out. Despite the limited number of measurement channels with broad spectral response, information on the chemical composition of the sample can be directly retrieved using chemometrics. The sensing capabilities for our spectral sensors are demonstrated with two practical application cases: determination of the nutritional information in raw milk and the classification of different plastic types. For both experiments, the photocurrent measurements from the sensor were used directly in regression or classification algorithms based on partial least squares analysis, leading to convincing prediction performance. This shows that the fabricated spectral sensor can retrieve chemical information for a broad set of sensing problems. Simulations have shown that for a specific sensing problem the prediction performance of the spectral sensor can be further improved by an optimized selection of the available spectral responses.
We have developed a near-infrared spectral sensor chip capable of classifying different materials and quantifying their composition. The core device consists of an array of pixels having distinct spectral responses covering the 900-1700 nm wavelength region. Each pixel consists of a resonant-enhanced photodetector comprising an absorbing layer and a tuning element embedded in a vertical cavity. The chip is based on a III-V/Silicon hybrid technology and enables easy customization of the wavelength response and high responsivity. Its robustness, small dimensions and single-shot operation make this sensor suitable for portable spectroscopic applications in the agro-food sector.
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