This paper presents a temperature-dependent SPICE model for SiC power MOSFETs. The model describes the static and dynamic behavior and accounts for leakage current and impact ionization. The technology-dependent MOSFET parameters are extracted from characterization measurements and datasheets. SPICE standard components and analog behavior modeling blocks are adopted for the model implementation. The model ensures a good agreement with experimental data over a wide temperature range, even under out-of-safe-operating-area conditions close to the failure occurrence.

Thanks to the increasing availability of SiC MOSFETs with outstanding static and dynamic performances, the number of applications in which these devices are used is rapidly growing. Despite that, the maximum current rating of such devices is usually limited at few hundred Amps, which sets an upper bound for the power level at which these transistors can be adopted. A viable solution to this problem consists in paralleling several SiC MOSFETs. However, the design of parallel configurations needs optimization since mismatched performances can cause the enhanced stress of a subset of devices, which can ultimately lead to premature failure of the whole module. In this contribution, several sets of Monte Carlo electrothermal simulations of parallel SiC MOSFETs are used to systematically relate the deviations of devices and circuit parameters to the resulting uneven power dissipation. To this purpose, a set of relevant parameters is identified and statistically described. Thereafter, the simulation of a 200 kHz synchronous buck converter relying on mismatched parallel MOSFETs is performed as case study. Eventually, a methodology to derive a guideline for the design of reliable multichip configurations is developed. The methodology is based on the iteration of Monte Carlo simulations for different tolerances of the parameters.

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.

The reduction of the trap density at the SiC/SiO2 interface of a SiC MOSFET is still an open issue for the next generations development. Since TCAD simulations are one of the most powerful tools adopted in the field of power semiconductor devices, in this work we define the guidelines for the calibration of the TCAD model from the point of view of the interface traps modeling. We have carried out a qualitative analysis of the effect of the SiC/SiO2 interface traps properties on the C-V curve by means of TCAD simulations, to support the interpretation of the experimental C-V curves of a SiC MOSFET. A new approach for the simulation of the C-V curves of a SiC MOSFET is proposed as well. The aim of the paper is the analysis of the effect of the SiC/SiO2 interface traps on the C-V curve by means of TCAD simulations.

The employability of photonics technology in the modern era’s highly demanding and sophisticated domain of aerospace and submarines has been an appealing challenge for the scientific communities. In this paper, we review our main results achieved so far on the use of optical fiber sensors for safety and security in innovative aerospace and submarine applications. In particular, recent results of in-field applications of optical fiber sensors in aircraft monitoring, from a weight and balance analysis to vehicle Structural Health Monitoring (SHM) and Landing Gear (LG) monitoring, are presented and discussed. Moreover, underwater fiber-optic hydrophones are presented from the design to marine application.