In this article, the failure mechanism and improved designs of silicon carbide (SiC) metal–oxide–semiconductor field-effect transistor (MOSFET) under high dV/dt switching condition were studied. Two typical structures of the main junction region were studied, and an improved new structure was proposed. Since the space charge regions of cell, termination, and main junction were charged and discharged, respectively, the corresponding transient current will generate a transient voltage and, hence, form the additional transient electric field to the gate oxide. In the first structure, the transient electric field spike appeared at the corners of the gate oxide layer and the field oxide layer. This typical failure mode was considered to be the degradation cause of gate oxide. In the second structure, all the transient electric fields of the oxide layers, including the part at the corner between gate oxide and field oxide layer maintained at a high level, and the typical failure mode were found to be device burnout. To improve the reliability, a novel main junction structure with an isolation structure design was proposed. It could successfully inhibit the transient high electric field when the device operates at dV/dt above 100 V/ns. The TCAD simulation results validated the detailed physical mechanism and showed that the transient electric field was significantly reduced by splitting the transient currents by the proposed main junction structure.

A low value of Schottky barrier height (SBH) ( $\Phi _{\textit{B}}\text{)}$ is highly desired to further reduce the power loss in 4H-SiC junction barrier Schottky (JBS) diodes; however, the reduced $\Phi _{\textit{B}}$ results in serious leakage current ( $\textit{I}_{\textit{R}}\text{)}$ when blocking high voltages. In this work, a tunable low $\Phi _{\textit{B}}$ of 0.92–0.98 eV with an ideal factor $\textit{n}$ $=$ 1.02–1.03 was first realized through effectual rapid thermal annealing (RTA) methods and combined with a trench-assisted stage-style p-type implants taking advantage of dead region for current flow to resolve the $\textit{I}_{\text{R}}$ issue. Numerical simulation demonstrated the superiority of this promising strategy in alleviating the Schottky interface electric field ( $\textit{E}_{\text{S}}\text{)}$ and suppressing $\textit{I}_{\textit{R}}$ without constraining the forward current flow. Based on this practical strategy, the fabricated device achieved one order of magnitude less $\textit{I}_{\textit{R}}$ at 25 $^{\circ}$ C and 220% enhancement of breakdown voltage ( $\textit{V}_{\textit{B}}\text{)}$ at 175 $^{\circ}$ C in contrast to the conventional low-barrier one. In addition, benefiting from the low $\Phi _{\textit{B}}$ , the device achieved an ultralow turn-on voltage of 0.47 eV and a remarkable forward voltage drop ( $\textit{V}_{\textit{F}}\text{)}$ of 1.36 V extracted at 20 A, indicating 14% lower conduction loss than the titanium-based JBS diodes with the same device configuration. This novel device structure incorporating low-barrier Schottky contact metal and trench-assisted stage-style p-implants (SPs) in the dead region of current flow suggests a promising way to develop more advanced 4H-SiC JBS diodes in the future.

This study proposes the use of a Quadruple Deep Q-Network (QDQN) for optimizing the energy management strategy of Plug-in Hybrid Electric Vehicles (PHEVs). The aim of this research is to improve energy utilization efficiency by employing reinforcement learning techniques, with a focus on reducing energy consumption while maintaining vehicle performance. The methods include training a QDQN model to learn optimal energy management policies based on vehicle operating conditions and comparing the results with those obtained from traditional dynamic programming (DP), Double Deep Q-Network (DDQN), and Deep Q-Network (DQN) approaches. The findings demonstrate that the QDQN-based strategy significantly improves energy utilization, achieving a maximum efficiency increase of 11% compared with DP. Additionally, this study highlights that alternating updates between two Q-networks in DDQN helps avoid local optima, further enhancing performance, especially when greedy strategies tend to fall into suboptimal choices. The conclusions suggest that QDQN is an effective and robust approach for optimizing energy management in PHEVs, offering superior energy efficiency over traditional reinforcement learning methods. This approach provides a promising direction for real-time energy optimization in hybrid and electric vehicles.

A method considering thermal boundary conditions and thermal coupling effects is proposed to estimate the junction temperature of double-sided cooling insulated-gate bipolar transistor (IGBT) modules. Traditional methods, which rely on negative temperature coefficient (NTC) measurements, often overlook mutual thermal interactions among chips, leading to inaccuracies under varying cooling boundary conditions. In this paper, a Foster thermal network model incorporating chip thermal coupling is developed to estimate the junction temperature of double-sided cooling IGBT power modules. The thermal model parameters are extracted through a combination of finite element simulation and experimental analysis. The effects of different cooling boundary conditions on the thermal model and the module’s heat channeling behavior are examined, and compensation strategies for various cooling boundaries are proposed. Experimental and simulation results indicate that the estimated junction temperature error of the proposed method remains within 5 °C under different operating conditions.

An energy management system is crucial for optimizing the performance and reducing fuel consumption of Plug-in Hybrid Electric Vehicles (PHEVs), which plays an important role in sustainable transportation. This paper presents a comprehensive study of the energy management problem for a selected PHEV, outlining key vehicle parameters such as battery state-of-charge (SOC), engine power, and energy consumption patterns. A mathematical formulation of the energy management problem is proposed to optimize fuel consumption while adhering to the vehicle’s operational constraints. The core of the paper focuses on the application of the Asynchronous Advantage Actor-Critic (A3C) algorithm to address this problem. To enhance the learning process, eligibility traces are introduced to refine the advantage function by weighting historical actions. This adjustment improves the decision-making process by making the action distribution more concentrated, which is crucial for optimizing fuel consumption. The proposed Asynchronous Advantage Actor-Critic Eligibility Traces (A3CET) algorithm incorporates these modifications and shows a significant improvement in fuel efficiency. During the training phase, it achieves 93.6% of the fuel optimality of the Dynamic Programming (DP) benchmark, and maintains 91.5% of DP’s performance during the testing phase. Moreover, A3CET reduces computation time by 62.5% compared to DP, and outperforms other EMSs in terms of both efficiency and real-time applicability.

Silicon carbide (SiC) MOSFETs are critical for next-generation power electronics, yet their reliability is challenged by alternating-current Bias Temperature Instability (AC BTI). While charge trapping and Recombination-Enhanced Defect Reaction (REDR) are known degradation pathways, the specific role of gate oxide thickness in determining the dominant mechanism remains unclear. This study investigates the degradation behaviors of SiC MOSFETs with varying oxide thicknesses under 150 kHz Dynamic Gate Stress. By maintaining a constant electric field, we decouple the effects of oxide thickness using high-frequency C-V, quasi-static gate current (I_GS) characteristics, and transconductance analysis. Results reveal that thin-oxide devices exhibit parallel C-V shifts and stable transconductance, indicating degradation driven by deep-level charge trapping. Conversely, thick-oxide devices display significant C-V stretch-out, negligible I_GS peak shifts, and severe transconductance degradation, accompanied by irreversible threshold voltage drift. We conclude that despite identical electric fields, the higher driving voltages in thick-oxide devices trigger severe interface state generation consistent with the REDR model, whereas thin-oxide devices are dominated by bulk oxide trapping. These findings highlight the necessity of thickness-dependent optimization strategies for SiC power devices.