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.

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We present an efficient and flexible method to realize micro- and nano-optical structures on the tip of optical fibers. We demonstrate this approach for a fiber-tip sensor consisting of a photonic crystal (PhC) structure in a semiconductor membrane on the cleaved facet of a telecom fiber. The PhC structure is fabricated on a wafer by lithography and etching and then transferred to the fiber facet by a simple mechanical pickup process through an opening in the substrate, without the need for adhesives or a micromanipulator. Due to its reliability, scalability, and the use of wafer-scale fabrication methods, this process increases the possibilities for fiber-tip applications at the industrial level. With the fabricated fiber tip sensors, we demonstrate sensing of the refractive index and temperature, with resonance wavelength shifts of 120 nm/RIU and 95 pm/K, respectively.

Near-infrared (NIR) spectroscopy is widely used for the classification of materials and the quantification of their properties. Today, there is a high demand for extending the use of this technique to portable applications, and eventually, the integration with consumer appliances and smartphones. To reach this goal, the overall size of the NIR sensor, its production cost, robustness, and resistance to vibrations are of particular importance. This paper describes an approach to spectral sensing in the NIR (850–1700 nm) using a handheld sensor module based on a fully integrated multipixel detector array with a footprint of around 2×2 mm2. The capabilities of the spectral sensor module were recently evaluated in two application cases: Quantification of the fat percentage in raw milk and the classification of plastic types. Fat quantification was achieved with a root mean square error (RMSE) of prediction of 0.14% and classification of plastic types was achieved with a prediction accuracy on unknown samples of 100%. The results demonstrate the feasibility of the direct NIR sensing approach used by the integrated sensor, which has potential to be used in a variety of applications.

A static filter array is developed based on Fabry-Perot cavities operating in the mid-infrared range from 2 to 4.5 µm, matching the absorption region of several gases relevant for air quality monitoring. The filters include two distributed Bragg reflectors and a high-index tuning element with varying thickness in between. The filters display high peak transmittance (> 80%) and controllable peak width. The robustness, ease of fabrication and the possibility to tune the optical response to a specific application make the integrated filter arrays suitable for compact sensing systems.