Near Infrared Efficiency Enhancement of Silicon Photodiodes by Integration of Metal Nanostructures Supporting Surface Plasmon Polaritrons.

Recent years have witnessed a growing interest in detectors capable of detecting single photons in the near-infrared (NIR), mainly due to the emergence of new applications such as light detection and ranging (LiDAR) for, e.g., autonomous driving. A silicon single-photon avalanche diode is surely one of the most interesting and available technologies, although it yields a low efficiency due to the low absorption coefficient of Si in the NIR. Here, we aim at overcoming this limitation through the integration of complementary metal-oxide-semiconductor (CMOS) -compatible nanostructures on silicon photodetectors. Specifically, we utilize silver grating arrays supporting surface plasmons polaritons (SPPs) to superficially confine the incoming NIR photons and therefore to increase the probability of photons generating an electron-hole pair. First, the plasmonic silver array is geometrically designed using time domain simulation software to achieve maximum detector performance at 950 nm. Then, a plasmonic silver array characterized by a pitch of 535 nm, a dot width of 428 nm, and a metal thickness of 110 nm is integrated by means of the focused ion beam technique on the detector. Finally, the integrated detector is electro-optically characterized, demonstrating a QE of 13% at 950 nm, 2.2 times higher than the reference. This result suggests the realization of a silicon device capable of detecting single NIR photons, at a low cost and with compatibility with standard CMOS technology platforms.

Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis.

The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications.