The high quality of crystal growth and advanced fabrication technology of silicon carbide (SiC) in power electronics enables the control of optically active defects in SiC, such as silicon vacancies (V_Si). In this paper, V_Si are generated in hexagonal SiC (4H) samples through ion implantation of nitrogen or (and) aluminum, respectively the n- and p-type dopants for SiC. The presence of silicon vacancies within the samples is studied using cathodoluminescence at 80K. For 4H-SiC samples, the ZPL (zero phonon line) of the V1′ center of V_Si is more intense than the one for the V1 center before annealing. The opposite is true after 900 °C annealing. ZPLs of the divacancy defect (V_CV_Si) are also visible after annealing.

This paper presents for the first time a comparison between experimental measurements of Optical Beam Induced Current (OBIC) and finite element simulations on high-voltage bipolar diodes. Two peripheral protection structures were chosen: a simple MESA protection and a MESA + JTE combination. Comparable experimental and simulated results were obtained in both cases.

In this paper, the static and dynamic characterization of a High Voltage (10kV) 4H-SiC Bipolar Junction Transistor (BJT) is presented. Using a high-voltage source in vacuum conditions, a breakdown voltage of 11 kV was measured. Results showed that both large and small BJTs exhibit similar on-state resistance per unit area and collector current density of 55 A.cm^-2. The current gain increases with a decrease in temperature, indicating reduced charge carrier recombination at lower thermal energies. Also, BJT have been characterized in switching mode at 1 kV. The study concludes that 4H-SiC BJT demonstrates promising electrical performance for high-efficiency applications in harsh environments.

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence.