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With the fabulous growth of the renewable energy industry, silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (mosfets) are increasingly finding applications in various scenarios. To ensure the safe operation of SiC mosfets, there is a pressing need for fast and reliable short-circuit protection methods. This article aims to give a detail overview of the existing short-circuit protection technologies for SiC mosfets regarding their merits and limitations. First, the challenges associated with short-circuit protection are comprehensively analyzed. Subsequently, thorough comparisons of state-of-the-art short-circuit detection methods and soft turn-off strategies are conducted, respectively. Finally, this work outlines the prospects of short-circuit protection technologies in the aspects of deficiencies and optimization of short-circuit protection in multichip power module with paralleled SiC mosfets.

Surge current capability of paralleled silicon carbide (SiC) metal-oxide-semiconductor-field-effect transistors (mosfets) operating in both first and third quadrants is required in various applications. The surge current distribution in paralleled SiC mosfets during third quadrant operation needs further investigations. This article, therefore, establishes a source–drain resistance model of SiC mosfets under different gate bias in surge current range, which reveals the current “competition mechanism” between the MOS-channel path and the body diode path under surge current conditions. It then investigates the influence of device parameters discrepancy on surge current distribution in paralleled SiC mosfets. It finds out that the discrepancy of body diode parameters has significant influences on surge current distribution under different gate biases, while the parameter discrepancy of MOS-channel has much smaller impact on surge current distribution, even with positive gate bias. The conclusions of this article are supported with simulation and experimental results.

Ghosting effects typically appear on glass surfaces, as each piece of glass has two contact surfaces causing two slightly offset layers of reflections. In this paper, we propose to take advantage of this intrinsic property of glass surfaces and apply it to glass surface detection, with two main technical novelties. First, we formulate a ghosting image formation model to describe the intensity and spatial relations among the main reflections and the background transmission within the glass region. Based on this model, we construct a new Glass Surface Ghosting Dataset (GSGD) to facilitate glass surface detection, with $ \sim 3.7K$∼3.7K glass images and corresponding ghosting masks and glass surface masks. Second, we propose a novel method, called GhostingNet, for glass surface detection. Our method consists of a Ghosting Effects Detection (GED) module and a Glass Surface Detection (GSD) module. The key component of our GED module is a novel Double Reflection Estimation (DRE) block that models the spatial offsets of reflection layers for ghosting effect detection. The detected ghosting effects are then used to guide the GSD module for glass surface detection. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. We will release our code and dataset.