The aim of this paper is to investigate the influence of gate resistance ($R_{G}$) and junction temperature on the dynamic performance of a buck converter. A simulation-based study of the performance of the SiC MOSFET in a DC-DC converter using LTSPICE has been done. The study focuses on the dynamic behavior of the device by assessing the drain-source voltage, drain current and the gate-source voltage under various operating scenarios. The effects on switching transients, waveform properties, and power losses are investigated. This work demonstrates that while operating conditions have an impact on conduction characteristics and loss distribution, variances in gate control have a considerable impact on switching speed variations, voltage transition, and current commutation. The results show how operating conditions impact SiC MOSFET-based power converters and offer valuable insights for gate driver design and temperature optimization.

As power densities in Wide Band Gap (WBG) semiconductors increase, traditional steady-state cooling methods often fail to address the rapid thermal transients. This paper investigates a diversity of cooling architectures for discrete Silicon Carbide (SiC) MOSFETs, and a frequency-dependent thermal characterization of different cooling technologies. This paper also presents a new measurement method of heat transfer through multilayer systems, which determines the thermal diffusivity of the power device in real dynamic operation. The measurement of the stored energy in the active component using temperature sensors proves itself to be a practical, rapid, low-cost, and non-invasive method. The results show that the active Peltier stage significantly allows for more frequent current bursts before reaching the thermal limit, thereby proving the system’s capability in high-frequency transient applications to be thermally stable. The analysis of different power systems with different types of cooling, for a range of frequencies of the drive signal for SiC MOSFETs, enables power electronic engineers to evaluate the efficacy of the cooling systems related to the operating frequency and not only as a function of the dissipated power. Analysis of thermal faults in MOSFETs with SEM/EDS methods reveals the weak points in the design of these WBG devices.

The aim of this paper is to perform a comprehensive simulation-based analysis of a DC-DC boost converter employing various SiC MOSFETs, using LTSPICE. The research investigates how device features affect converter performance in terms of power losses, thermal behavior, and total harmonic distortion (THD) while the converter is operating in continuous conduction mode (CCM). A range of switching frequencies, and output power levels are selected to generate to assess converter performance. The research shows how thermal stress, conduction and switching losses, and harmonic spectra are affected by switching dynamics and device parasites. The findings provide significant insights for the best choice of SiC MOSFETs in high-efficiency DCDC converter design by highlighting the trade-offs between fastswitching devices and electromagnetic performance.

This work reports for the first time the fabrication of metallic nanoparticles from platinum material (Pt-NPs) implying pulsed laser ablation in choline chloride-ethylene glycol deep eutectic solvent (DES) as well as in their mixtures with ethanol in a 1:3 ratio as liquid media. KrF excimer laser (λ = 248 nm) has been used as an irradiation source. The Pt target is placed in a Teflon pad on the bottom of a beaker and immersed in the corresponding liquid. The created Pt-NPs by pulsed laser ablation are dispersed in the surrounding medium, forming a colloid with it, and are characterized by the detailed methods described. A synergistic effect is achieved by combining the nontoxicity of the liquid and the biocompatibility of the Pt-NPs for the fabrication of very small, nonaggregated NPs, applicable, also, in the medical field. The peculiarities of the liquid do not allow the production parameters to be varied in wide ranges. The investigation of the Pt-NPs' optical characteristics by UV/vis spectroscopy showed the existence of three bands. The origins of two of the bands are presumably due to interband transition and plasmonic absorption, respectively. The origin of the third band needs further in-depth investigation. The morphological properties were studied through high-resolution scanning transmission electron microscopy (HR-STEM). The minimum value of the mean size of 2.2 nm is achieved at 7 Hz RR and 40 min ablation time when the pure mixture of choline chloride-ethylene glycol, denoted as ILEG, is used. In support, the obtained Pt-NPs were analyzed by direct analysis in real time mass spectrometry (DART-MS) to demonstrate their existence in the colloidal solution. The face-centered cubic (FCC) phase for the Pt-NPs with the predominant orientation of the (220) crystalline plane was determined by X-ray diffraction (XRD). FTIR and Raman spectroscopy measurements did not show the existence of additional bonds between pure ILEG and Pt-NPs. One application of the synthesized NPs was illustrated by the increased sensitivity of detecting methylene blue through the surface-enhanced Raman spectroscopy (SERS) method.

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.

The use of wide-bandgap semiconductor-based transistors has become essential in recent years for the development of new systems for sustainable mobility and, more generally, for energy conversion. In this frame, silicon carbide-based power MOSFETs are currently the most promising devices, as they enable the simultaneous management of high voltages and high currents. During the switching phases, the device experiences current pulses, which, due to Joule heating, lead to thermomechanical expansion processes caused by the generated heat. This process represents one of the main aging mechanisms for this class of devices, whose failure mechanisms may significantly differ from that of silicon-based devices. This work proposes an analysis protocol to thermally and thermomechanically characterize a commercially available SiC power MOSFET. To this end, the device was subjected to periodic stress cycles to realistically simulate its aging process as closely as possible. To assess the device's health status, its dynamic ON resistance, measured during a test pulse, was monitored. This choice is motivated by the possibility of tracking this parameter during normal system operation, thus enabling a possible estimation of the device's remaining lifetime on the field.

In this paper, a liquid-to-liquid thermal shock test is performed on TO-247 packages. By employing liquid with a high heat transfer coefficient as an energy transfer medium, it becomes possible to quickly couple thermomechanical stresses at the module interface. The temperature jump is achieved by maintaining the hot chamber at 150°C and the cold one at −65°C, by immerging the samples in each chamber for 8 minutes. Using ANSYS Mechanical, a numerical model was developed to evaluate the lifetime of the TO-247 under thermal shock test. In addition, this model was also used to compare the lifetime of these packages when a temperature cycle is considered. Experimental results showed differences between cold and hot ramp rate. In more detail, the average ramp rate of the 4 samples in hot environment is about 90°C/s while in the cold one it is about 65°C/s. Difference between heating and cooling rate could be addressed to the temperature effect on fluid viscosity. The numerical results show that the thermal shock test (with a soak time of 5 minutes) anticipates crack initiation compared to the temperature cycle (with the same soak time), suggesting that the difference in accumulation of inelastic work due to fatigue lies in the different ramp rates. A further comparison between two thermal shock was done, decreasing the soak time to 2 minutes, which confirmed the previous results.

Micro-Raman spectroscopy was carried out to evaluate the localized residual stresses in commercial Gallium-Nitride-based devices, specifically, AlGaN/GaN high-electron-mobility Transistors (HEMTs) with a novel packaging design provided by STMicroelectronics S.r.l. (Catania, Italy). The packaging plays a key role in protecting the device core against the external environment, thus minimizing damages caused by mechanical shocks, exposure to light, and contact with chemicals, conjointly achieving an efficient heat dissipation rate. Even though the packaging is a required step for the proper functioning of ready-to-use electronic devices, its application typically may introduce mechanical stress to AlGaN/GaN HEMTs, which can result in various reliability issues. In this paper, we investigate the impact of packaging on residual stress by analyzing the frequency shift of the E2 Raman peak along GaN layers and at the GaN/Si interface. An extensive evaluation was conducted using both a packaged device and a wafer-level device. The correlation between Raman frequency shifts of the E2 mode was accurately quantified, revealing a stress mitigation of approximately 0.1 GPa. This reduction is ascribed to the compressive stress introduced by the packaging, which partially offsets the intrinsic tensile stress of the wafer-level device. The proposed methodology could, in principle, be implemented to improve the development of packaging.

This paper presents an experimental thermographic analysis of a directly-cooled silicon-carbide based power module, designed for automotive traction applications. The aim of this activity is related to one of the reliability assessments executed on power MOSFETs arranged in this package, named active power cycle. In this framework, thermo-sensitive electrical parameters (TSEPs) are commonly used to estimate the junction temperature of power devices. In the paper, the TSEP related to the body diode voltage drop is compared with the results obtained by using a high frame-rate thermal camera. Moreover, the heat propagation inside the module during the heating phase is showed.

This work presents the synthesis of CoSb3 one-dimensional (1D) thermoelectric nanomaterials using electrodeposition under galvanostatic conditions and polycarbonate membranes as a template (50 nm diameter pores). Cyclic voltammetry measurements have been performed to get preliminary information on the electrochemical reduction process of the involved species. Different current density values in the range 1-4 mA cm(-2) have been applied, leading to the formation of nanowires (NWs) and micro- and nanomushroom caps, as evidenced by the scanning electron microscopy and scanning transmission electron microscopy investigations. Through fine-tuning of the current density the desired Co/Sb atomic ratio could be achieved. Energy-dispersive X-ray spectroscopy analysis showed the formation of CoSb3 at 1.4 mA cm(-2), and it has also been confirmed by high-resolution transmission electron microscopy and micro-Raman spectroscopy. In this work, we present for the first time the fabrication of a CoSb3-CoxSby heterojunction on the same NW exhibiting Sb-rich and Co-rich alloy segments, prepared by electrodeposition from the same electrolyte by simply varying the applied current density.

Within the framework of the assisted systems for automotive applications, considerable research has been employed to monitoring the driver's attention level in order to assess the risk level of the driving scenario. In this context, physiological monitoring of the driver's condition has emerged as a relevant approach to enhance driving assistance without having an invasive approach. According to these premises, the authors have developed a driving assistance system capable to employ a dedicated bio-sensor for capture the driver's photoplethysmo-graphic (PPG) signal, which is closely linked to their level of attention. This PPG signal is then processed by a dedicated deep learning architecture to reconstruct the driver's attention level. Meanwhile a separate automotive-grade intelligent vision-based system has been developed to quantify the risk level of the driving scenario by means of a video saliency analysis technique. The effectiveness of this comprehensive system has been validated through experimental results.

Detecting and identifying production defects in the semiconductor industry are crucial for maintaining quality control during manufacturing. The use of new materials, such as Silicon and Silicon Carbide, highlights the need for a reliable wafer defect detection system. The Electrical Wafer Sorting (EWS) stage, which involves an electrical analysis of defect maps, is effective in spotting anomalies and defect patterns on wafers. This phase is time-consuming but enables semiconductor companies to improve and optimize their manufacturing processes, including the integration of advanced deep learning techniques. The proposed pipeline aims to meet the demand for a fully automated system to identify manufacturing defects in wafers, utilizing intelligent analysis of EWS wafer maps in combination with a Deep Convolutional Neural Network and an unsupervised subsystem. The stringent level of intelligent control at the EWS stage is necessary because the devices produced from the analyzed wafers are mainly power devices used in the power and inverter systems of the latest generation of conventional and electric cars. Experimental results have confirmed the effectiveness of this approach.

Power modules development is becoming more and more important for switching applications, to improve electric and reliability performances. This paper presents an experimental-numerical method to characterize the reliability behavior of ACEPACK(Trade Mark) SMIT package, a top side-cooled module designed with a half bridge topology, which employs high-voltage silicon MOSFETs. The experimental test involves power cycling to study the reliability and thermal behavior. Then, a finite-element based model is developed to simulate test conditions, to calculate temperature behavior inside the package.

In automotive and industrial application domains, the "health monitoring" or predictive reliability of electronic devices plays a key-role in ensuring reliability of the electrical components. Health monitoring regards a collection of methods aimed at monitoring the operational features and performance of an electronic components in order to schedule preventive/predictive maintenance. Similarly, predictive reliability makes it possible to test electronic devices in an intelligent manner, i.e. by identifying predictive patterns of degradation or anomalous behavior in order to effectively characterize the monitored systems. Specifically, the authors of the present work will show the excellent results obtained by applying artificial intelligence techniques to the monitoring by optical microscopy of the power modules in Silicon Carbide technology usually placed in the traction inverter of electric vehicles.. The Silicon-Carbide Power Module is a key component embedded in the traction inverter-system of the modern electric cars, and its functional degradation or unusual behavior may have a significant negative impact on the car’s safety. As with any electronic components, the power module is subjected to electro-mechanical stresses which impact its functionality over time, highlighting progressive states of physical degradation. One technique for detecting defects in Silicon-Carbide Power Modules is optical microscopy. Through ad-hoc deep learning system based on the use of an enhanced YOLOv5 architecture, the captured optical microscopic image of the analyzed Silicon-Carbide Power Module will be automatically processed providing in output a bounding-box segmentation of the most impactful and significant defect patterns for the functioning of the monitored power device. Several tests have been performed on different power modules of the ACEPACK TM DRIVE series delivered by STMicroelectronics. The collected performance (identification and classification accuracy close to 93%) confirmed the effectiveness of the proposed solution.

In automotive and industrial domains, the “health monitoring” or “condition monitoring” of electronic devices is gradually playing a key role in manufacturing processes and innovation roadmaps. The concept of health monitoring is often related to the so-called “residual lifetime” of the monitored system. In this work, the authors have designed a deep learning system for the health monitoring of power devices in Silicon Carbide (SiC) technology used in the Traction Inverter Systems of the latest generation electric cars. A Temporal Fusion Transformer embedding such layers of Temporal Convolutional Network with a Multi-Head Attention block for the robust lifetime assessment of SiC power devices, is proposed. Specifically, the designed system predicts such future samples of the ON-state voltage between drain and source of the low-side part of the SiC power module $Vds_{LS}$ , in half-bridge configuration. Extensive literature confirmed that the $Vds_{LS}$ signal can be efficiently used as a robust predictive device-degradation marker. Through the learning of the temporal feature relationships at different scales and the intelligent selection of relevant input features, the proposed solution will discard unnecessary input dynamics building a multi-step predictive model of the $Vds_{LS}$ signal, significantly more performing than the existing state-of-the-art architectures. The proposed deep pipeline has been tested on several ACEPACK DRIVE SiC power modules delivered by STMicroelectronics, with an average error of about 0.2%, confirming the effectiveness of the proposed system.

The recent improvement in semiconductor devices has led to higher efficiency and power density. SiC and GaN offer higher switching frequencies and lower losses; however, the knowledge of the behavior of these devices is not mature. In this paper, a system for fast charging of batteries for electric vehicles based on an isolated DC-DC converter equipped with both SiC and GaN devices is presented, and an experimental comparison among these two technologies will be given in terms of dynamic performances, electromagnetic compatibility, stability, efficiency.

In this work, a method to thermally characterize a power semiconductor module based on silicon carbide for traction inverters is presented. In general, this is important in order to determine the real thermal behavior of the system and, consequently, to forecast the application reliability performances. Thermal impedance is measured by a dedicated bench, capable to heat power module by applying a constant current, yet ensuring a controlled liquid cooling environment. The obtained experimental curve is then translated into a system made by several RC ports, obtained employing convolution’s algorithms. Solving the related differential equations, temperature profile can be obtained as function of time. Considering the actual application power loss, the related temperature profile can be calculated and collected in a discrete number of different load cases characterized by a duration and a thermal swing.

Wide bandgap material-based devices allow faster switching frequency and exhibit smaller losses than traditional Si devices; nevertheless, a complete understanding of the functioning of these new devices remains poorly understood. A fast battery charger for electric vehicles based on a converter employing SiC and GaN devices is here reported Besides, these two technologies are experimentally compared, in the same layout, to highlights their performance in terms of electrical dynamic and electromagnetic compatibility.

Automotive domain is making rapid growth in next generation cars development embedding higher levels of autonomy and intelligent assistance. Although the general advanced driver assistance system (ADAS) architectures are widely debated in the global automotive market, limited interaction between driver and these intelligent solutions sometimes make these solutions inefficient. For these reasons, the authors started an investigation about driver’s feedback with respect to the intelligent assistance inputs provided by the ADAS technologies. In this context, the goal of this proposal is to show the implemented intelligent system which learns from the analysis of the car driver’s eyes saccadic movements, the correlated level of attention towards the salient driving scene. With this approach, the authors were able to collect a kind of visual-feedback signal which learns the driver eye’s fixing dynamic associated to the analyzed driving scene. Through ad-hoc enhanced motion magnification technique, the authors were able to amplify the mentioned saccadic dynamics to allow a downstream deep classifier to associate this physiological behavior with the corresponding level of the driver attention. The collected performances (near to 97

Silicon dioxide (SiO2) layers deposited on 4H-SiC and subjected to different post deposition annealing (PDA) in NO and N2O were studied to identify the key factors influencing the channel mobility and threshold voltage stability in lateral implanted 4H-SiC MOSFETs. Cyclic gate bias stress measurements allowed to separate the contributions of interface states (Nit) and near interface oxide traps (NIOTs) in the two oxides. The reduction of these traps in the NO annealed sample is due to the lower amounts of sub-stoichiometric silicon oxide (~1nm) and carbon-related defects (<1nm) at the interface, as could be demonstrated by Electron Energy Loss Spectroscopy. The experimental results indicate that limiting the SiC re-oxidation during post-deposition annealing in MOSFET technology is a key factor to improve the mobility and threshold voltage stability.