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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.

Illicit-drug production, trafficking and seizures are on an all-time high. This consequently raises pressure on investigative authorities to provide rapid forensic results to assist law enforcement and legal processes in drug-related cases. Ideally, every police officer is equipped with a detector to reliably perform drug testing directly at the incident scene. Such a detector should preferably be small, portable, inexpensive and shock-resistant but should also provide sufficient selectivity to prevent erroneous identifications. This study explores the concept of on-site drugs-of-abuse detection using a 1.8 × 2.2 mm2 multipixel near-infrared (NIR) spectral sensor that potentially can be integrated into a smartphone. This integrated sensor, based on an InGaAs-on-silicon technology, exploits an array of resonant-cavity enhanced photodetectors without any moving parts. A 100% correct classification of 11 common illicit drugs, pharmaceuticals and adulterants was achieved by chemometric modelling of the response of 15 wavelength-specific pixels. The performance on actual forensic casework was investigated on 246 cocaine-suspected powders and 39 MDMA-suspected ecstasy tablets yielding an over 90% correct classification in both cases. These findings show that presumptive drug testing by miniaturized spectral sensors is a promising development ultimately paving the way for a fully integrated drug-sensor in mobile communication devices used by law enforcement.

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.

On-chip spectrometers with tailored spectral range and compact footprint have been pursued widely in the last decade. Splitting different frequencies typically requires a propagation length that scales inversely with the frequency resolution, which leads to a trade-off between resolution and size. Scattering media in the diffusive regime provide a long light path and multipath interference in a compact area, resulting in strong dispersive properties that can be used for on-chip compressive spectrometry. However, the performance suffers from the low light transmission through the diffusive medium. It has been found that there exist “open channels” such that light with certain wavefronts can pass through the medium with high transmission. Here we show that a scattering structure can be designed so that open channels match target input/output channels in order to maximize transmission while keeping the dispersive properties typical of diffusive media. Specifically, we use inverse design to generate a scattering structure where the open channels match the output waveguides at desired wavelengths. For a proof of concept, we propose a 1 × 10 multiplexer covering a band of 500 nm in the mid-infrared spectrum, with a footprint of only 9.4 µ m × 14.4 µ m . We also show that filters with nearly arbitrary spectral response can be designed, enabling a new degree of freedom in on-chip spectrometer design, and we investigate the ultimate resolution limits of these structures. The structures can also be designed based on a simple geometry consisting of circular holes with diameters from 200 to 700 nm etched in a dielectric slab, making them highly suited for fabrication. With the help of compressive sensing, the proposed method represents an important tool in the quest towards integrated lab-on-a-chip spectroscopy.

We present a novel approach to NIR spectral sensing using a handheld module based on a miniaturized integrated detector array. Applicability of the sensor is demonstrated in monitoring moisture in grains and classification of plastics.

We demonstrate a compact and robust near-infrared (900-1700nm) spectral sensor consisting of 16 resonant-cavity-enhanced photodetectors. We achieve a tunable spectral response with narrow linewidth and high responsivity and demonstrate the sensing performance on various applications.

Disordered photonic nanostructures have attracted tremendous interest in the past three decades, not only due to the fascinating and complex physics of light transport in random media, but also for peculiar functionalities in a wealth of interesting applications. Recently, the interest in dielectric disordered systems has received new inputs by exploiting the role of long-range correlation within scatterer configurations. Hyperuniform photonic materials, that share features of photonic crystals and random systems, constitute the archetype of systems where light transport can be tailored from diffusive transport to a regime dominated by light localization due to the presence of photonic band gap. Here, advantage is taken of the combination of the hyperuniform disordered (HuD) design in slab photonics, the use of embedded quantum dots for feeding the HuD resonances, and near-field hyperspectral imaging with sub-wavelength resolution in the optical range to explore the transition from localization to diffusive transport. It is shown, theoretically and experimentally, that photonic HuD systems support resonances ranging from strongly localized modes to extended modes. It is demonstrated that Anderson-like modes with high Q/V are created, with small footprint, intrinsically reproducible and resilient to fabrication-induced disorder, paving the way for a novel photonic platform for quantum applications.

The low throughput of atomic force microscopy (AFM) is the main drawback in its large-scale deployment in industrial metrology. A promising solution would be based on the parallelization of the scanning probe system, allowing acquisition of the image by an array of probes operating simultaneously. A key step for reaching this goal relies on the miniaturization and integration of the sensing mechanism. Here, we demonstrate AFM imaging employing an on-chip displacement sensor, based on a photonic crystal cavity, combined with an integrated photodetector and coupled to an on-chip waveguide. This fully-integrated sensor allows high-sensitivity and high-resolution in a very small footprint and its readout is compatible with current commercial AFM systems.

We propose a force sensor, with optical detection, based on a reconfigurable multicavity photonic molecule distributed over two parallel photonic crystal membranes. The system spectral behaviour is described with an analytical model based on coupled mode theory and validated by finite difference time domain simulations. The deformation of the upper photonic crystal membrane, due to a localized vertical force, is monitored by the relative spectral positions of the photonic molecule resonances. The proposed system can act both as force sensor, with pico-newton sensitivity, able to identify the position where the force is applied, and as torque sensor able to measure the torsion of the membrane along two perpendicular directions.

Conversion between signals in the microwave and optical domains is of great interest both for classical telecommunication and for connecting future superconducting quantum computers into a global quantum network. For quantum applications, the conversion has to be efficient, as well as operate in a regime of minimal added classical noise. While efficient conversion has been demonstrated using mechanical transducers, they have so far all operated with a substantial thermal noise background. Here, we overcome this limitation and demonstrate coherent conversion between gigahertz microwave signals and the optical telecom band with a thermal background of less than one phonon. We use an integrated, on-chip electro-optomechanical device that couples surface acoustic waves driven by a resonant microwave signal to an optomechanical crystal featuring a 2.7 GHz mechanical mode. We initialize the mechanical mode in its quantum ground state, which allows us to perform the transduction process with minimal added thermal noise, while maintaining an optomechanical cooperativity >1, so that microwave photons mapped into the mechanical resonator are effectively upconverted to the optical domain. We further verify the preservation of the coherence of the microwave signal throughout the transduction process. Electro-optomechanical conversion between optical and microwave photons is achieved with minimal added noise by cooling the mechanical oscillator to its quantum ground state. This has potential for future coherence-preserving transduction.

Near-infrared spectroscopy (NIR) is widely used today in applications ranging from monitoring industrial manufacturing processes to assessing the chemical composition and quality of products and materials. However, the challenge remains in reducing size and cost of these spectroscopic devices, while maintaining their sensitivity and robustness. The miniaturisation of spectral sensors is essential to expanding their application beyond dedicated stations in industrial settings and analytical labs, into the hands of non-specialists working on-site and eventually to consumers. The design of current portable NIR sensor systems is mostly inspired by conventional instruments that use gratings or interferometers. The drawback of these approaches is that they are complex to assemble and contain movable parts that are susceptible to shocks and mechanical vibrations.

Subwavelength sized on-chip coherent light sources are attractive for variety different applications, such as integrated optical interconnects [1] , super resolution imaging [2] and neuromorphic computing [3] . Practical demonstration of nanolasers brings a number of challenges, including the nanostructures sensitivity to the fabrication defects. High surface-to-volume ratio makes surface defects detrimental for lasing performance. To minimize the impact of non-radiative surface recombination, many surface passivation methods have been reported [4] . For InGaAs material, significant photoluminescence (PL) signal improvement has been observed for the combination of ammonium sulphide ((NH 4 ) 2 S) treatment and a thin SiO x layer coating deposited on the surface by plasma enhanced chemical vapour deposition (PECVD) [5] . Such passivation method may be promising for practical devices only if it is stable for high temperatures, since the fabrication of optoelectronic devices requires a number of high-temperature processes like rapid thermal annealing (RTA) for ohmic contacts formation. So far, the quality of the ammonium sulfide passivated surface has been shown to degrade upon annealing at temperatures above 300 °C [6] . In this work, we show photoluminescence performance improvement after ammonium sulphide and SiO x coating passivation and its stability in a subsequent high temperature treatment.

Sensing displacements at the nanoscale is the basis for many metrology applications, in particular atomic-force microscopy. Displacement sensing with nano-optomechanical structures provides interesting opportunities for integration, but it typically features a small dynamic range due to the near-field nature of the sensor-sample interaction. Here, a far-field sensing approach based on a grating coupler is considered and an analytical model used to tune its performance is introduced. The proposed model allows exploiting the full range of design parameters and thereby optimizing resolution and dynamic range. The compact size of the sensor and the possibility of integrating it with an on-chip laser and detector make it very promising for fully-integrated optical sensing systems.

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.

In this talk we will present recent process on the integration of nano-opto-electro-mechanical sensors with photonic circuits and optical read-out, showing a route towards fully-integrated optical sensing.

The paper focuses on Cross-Version Defect Prediction (CVDP) where the classification model is trained on information of the prior version and then tested to predict defects in the components of the last release. To avoid the distribution differences which could negatively impact the performances of machine learning based model, we consider Dissimilarity-based Sparse Subset Selection (DS3) technique for selecting meaningful representatives to be included in the training set. Furthermore, we employ a Convolutional Neural Network (CNN) to generate structural and semantic features to be merged with the traditional software measures to obtain a more comprehensive list of predictors. To evaluate the usefulness of our proposal for the CVDP scenario, we perform an empirical study on a total of 20 cross-version pairs from 10 different software projects. To build prediction models we consider Logistic Regression (LR) and Random Forest (RF) and we adopt 3 evaluation criteria (i.e., F-measure, G-mean, Balance) to assess the prediction accuracy. Our results show that the use of CNN with both LR and RF models has a significant impact, with an improvement of ∼20% for each evaluation criteria. Differently, we notice that DS3 does not impact significantly in improving prediction accuracy.

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.

We present a novel method to fabricate fiber-tip sensors. Using high-throughput semiconductor fabrication methods combined with a simple fiber-attachment scheme, we can mass-produce the sensors, preserving their surface unaltered. We experimentally demonstrate the sensor performance.

Recent theories proposed a deep revision of the well-known expression for the Purcell factor, with counterintuitive effects, such as complex modal volumes and non-Lorentzian local density of states. We experimentally demonstrate these predictions in tailored coupled cavities on photonic crystal slabs with relatively low optical losses. Near-field hyperspectral imaging of quantum dot photoluminescence is proved to be a direct tool for measuring the line shape of the local density of states. The experimental results clearly evidence non-Lorentzian character, in perfect agreement with numerical and theoretical predictions. Spatial maps with deep subwavelength resolution of the real and imaginary parts of the complex mode volumes are presented. The generality of these results is confirmed by an additional set of far-field and time-resolved experiments in cavities with larger modal volume and higher quality factors.