We present several integrated technologies on Silicon, from visible to mid-infrared, for particulate matter and gas detection. We present new concepts to detect in the visible particulate matter with a high sensitivity and a discrimination of both particle sizes and refractive indices. For gas detection, mid-infrared technologies developments include on one hand, microhotplate thermal emitters, as a cheap solution for gas sensing, eventually enhanced by plasmonics, and on the other hand quantum cascade lasers-based photoacoustic sensors, for high precision measurement, and for which the integration on Silicon is pushed forward for a reduction of costs.
Photoacoustic (PA) spectroscopy is among the most sensitive techniques used to monitor chemical emission or detect gas traces. In the mid-infrared, where most of gases of interest have their strongest absorption lines, this technique takes advantage of the high optical power and room temperature operation of quantum cascade lasers (QCL). We have recently demonstrated that centimeter-size PA cells can compete, with bulky commercial systems for gas sensing without any compromises on performances. We demonstrate a new step towards cost reduction, extreme integration, and mass deployment of such PA sensors with a miniaturized silicon PA-cell fabricated on standard CMOS tools. The design, fabrication and characterizations of this new sub-centimeter PA cell built on a silicon platform are presented. First, the component has been designed using a detailed physical model, accounting for viscous and thermal losses, and metamodel-based optimization techniques. Second, it has been fabricated on our 200 mm CMOS pilot line. Several wafers have been released and diced. Single chips have then been assembled with commercial capacitive microphones and finally characterized on our reference gas bench. The photoacoustic simulations and the acoustics experiments are in a good agreement. The tiny PA cell exhibits a sensitivity down to the ppm level for CO<sub>2</sub> at 2300 cm<sup>-1</sup>, as well as for CH<sub>4</sub> at 3057 cm<sup>-1</sup> even in a gas flow. Taking advantage of the integration of QCLs on Si and photonic circuitry, the silicon PA cell concept is currently being extended towards a fully integrated multigas detector.
The Mid-IR spectral range (2.5 μm up to 12 μm) has been considered as the paradigm for innovative silicon photonic devices. In less than a decade, chemical sensing has become a key application for Mid-IR silicon photonic devices because of the growing potential in spectroscopy, materials processing, chemical and biomolecular sensing, security and industry applications. Measuring in this spectral range, usually called molecule fingerprint region, allows to address a unique combination of fundamental absorption bands orders of magnitude stronger than overtone and combination bands in the near IR. This feature provides highly selective, sensitive and unequivocal identification of the chemicals.<p> </p> Progress in Cascade Laser technology (QCL and ICL) allows to select emission wavelengths suitable to target the detection of specific chemicals. With these sources, novel spectroscopic tools allowing real-time in-situ detection of gasses down to traces are nowadays commercially available.<p> </p> Mid-IR Si photonics has developed a novel class of integrated components leading to the integration at chip level of the main building blocks required for chemical sensing, i.e. the source, the PICs and the detector. Three main directions of improvement can be drawn: i) extend the range of wavelengths available from a single source, ii) move beam handling and routing from discrete optics to PICs and iii) investigate detection schemes for a fully integrated on-chip sensing.<p> </p> This paper reviews recent key achievements in the miniaturization and the co-integration of photonics devices at chip and packaging level to address cost, size and power consumption. Perspectives on potential applications will also be presented.
Focal cooling is a promising alternative therapy for intractable focal epilepsies, avoiding the irreversible neuronal damages induced by resection surgery. However, due to thermal conduction losses, local cooling of a deep brain region remains a challenging objective for thermoelectric or fluidic technologies. Here, we investigated the viability of an optical micro-cooler based on anti-Stokes refrigeration of ytterbium doped YLF crystals, taking into account the medical constraints for implantable device. We realized significant cooling under atmospheric pressure and developed a solution drastically reducing the harmful fluorescence heating of brain-like liquids below 2 K, thus demonstrating the relevance of this technology for biomedical applications.
In the framework of Fluorescence-enhanced Diffuse Optical Tomography, a numerical approach (usually the Finite Element Method) is often required because of the complexity of the geometry of the diffusing systems studied. This approach is appropriate for handling problems modelled by elliptic coupled partial differential equations but is known to be time and memory consuming. The resolution of the adjoint problem considerably speeds up the treatment and allows a full 3D resolution. Nevertheless, because of the ill-posedness of the problem, the reconstruction scheme is sensitive to <i>a priori</i> knowledge on the parameters to be reconstructed. In the present work, a multiple step, self-regularized, reconstruction algorithm for the spatial distribution of the fluorescent regions is presented. The prior knowledge of the regions of interest is introduced via a segmentation. This one is performed on the results obtained with a first rough reconstruction. The results are then refined along iterations of the segmentation/reconstruction scheme. The technique is tested on experiments performed with a home made tomographer. A phantom study is presented.
A discussion on recent works on diffusive inverse problems is presented with a special focus on three-dimensional imaging methods and their application to small animal imaging by fluorescence-enhanced Diffuse Optical Tomography. A numerical approach using the Finite Element Method for handling problems modelled by elliptic coupled partial differential equations is justified by the complexity of the geometry of the system but is known to be time- and memory-consuming. The resolution of the adjoint problem considerably speeds up the treatment and allows a full 3D resolution. Nevertheless, because of the ill-posedness of the problem, the reconstruction scheme is sensitive to <i>a priori</i> knowledge on the parameters to be reconstructed. In this study, a multiple step, self-regularized, reconstruction algorithm for the spatial distribution of the fluorescent regions is presented. We introduce the prior knowledge of the regions of interest <i> via</i> a segmentation of the results performed with a first rough reconstruction of the fluorescent regions. The results are then refined along iterations of the segmentation/reconstruction scheme.