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Considering potential applications related to superconductivity and aerospace (typically less than 100 K), in this article, the temperature dependence of silicon carbide (SiC) power diodes is systematically characterized and analyzed over a wide temperature range of 90–478 K, especially focusing on cryogenic temperature. First, the static performance degradation mechanism of SiC diodes is established in an ultrawide temperature range, including forward/reverse I-V characteristics and junction capacitance (Cj) characteristics. Second, the reverse recovery characteristics are achieved, including peak reverse recovery current (Irm), reverse recovery charge (Qrr), and switching energy (Esw), clarifying a clearer internal relationship between reverse recovery and junction temperature. Meanwhile, the aforementioned critical parameters are further analyzed on an electrical scale with normal atmosphere temperature, including switching speed range of 62.3–2054.8 A/μs, load current range of 6–30 A, and dc voltage range of 400–1000 V. Third, based on newly proposed power loss analysis method, the continuous operation performance of SiC diodes is quantified and analyzed in actual cryogenic converters. The excellent temperature dependence indicates that SiC diodes have great superiority for extreme applications. Importantly, SiC mosfet's body diode shows the great potential to operate as a freewheeling diode in the compact converter, especially at cryogenic temperature. [ABSTRACT FROM AUTHOR]

Multi-media data including image, video, text, audio, and 3D model, has been fast emerging on the Internet. Jointly correlating the data of various media types is a challenging task. With the considerable learning ability of deep network, existing works mainly construct multi-pathway network to learn cross-media correlation, where each pathway is for one media type. However, with number of media types increasing, existing methods face the problems of high repetition and complexity, leading to overfitting and poor generalization ability, which makes adverse effect on correlation learning. For addressing the above issues, we propose cross-media deep compression and regularization (CDCR) approach for quintuple-media joint correlation learning: 1) cross-media partial weight-sharing networks is proposed, where a part of parameters are commonly shared among multiple pathways, to exploit common characteristics across different media types for capturing intrinsic cross-media correlation; 2) we propose media-adaptive network pruning to drop connections between weakly-correlated neurons, which can emphasize media-specific characteristics adaptively; and 3) cross-media network regularization is proposed to utilize relationships among quintuple-media data, which can guarantee generalization ability and enhance intra-media and inter-media correlation. The experiments verify the effectiveness of our approach, which outperforms the state-of-the-art methods on two very challenging datasets, including a large-scale dataset PKU XMediaNet with more than 100 000 quintuple-media instances. [ABSTRACT FROM AUTHOR]

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform cross-media retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning cross-media correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach. [ABSTRACT FROM AUTHOR]

The heterogeneity gap leads to inconsistent distributions and representations between image and text, which rises a challenging task to measure their similarities and construct cross-media correlation between them. The existing works mainly model the cross-media correlation in a common subspace, which causes insufficient correlation modeling in such third-party subspace with intermediate unidirectional transformation. Inspired by the recent advances of neural machine translation, which aims to establish a corresponding relationship between two entirely different languages, we can naturally discover that it has striking common characteristic with cross-media correlation learning to consider image and text as bilingual pairs, where the image is treated as a special kind of language to provide visual description, so that bidirectional transformation can be conducted between image and text to effectively explore cross-media correlation in the feature space of each media type. Thus, we propose a reinforced cross-media bidirectional translation (RCBT) approach to model the correlation between visual and textual descriptions. First, cross-media bidirectional translation mechanism is proposed to conduct direct transformation between the bilingual pairs of visual and textual descriptions bidirectionally, where the cross-media correlation can be effectively captured in both feature spaces of image and text through bidirectional translation training. Second, cross-media context-aware network with residual attention is proposed to exploit the rich spatial and temporal context hints with cross-media convolutional recurrent neural network, which can lead to more precise correlation learning for promoting bidirectional translation process. Third, cross-media reinforcement learning is proposed to perform a two-agent communication game played as a round between image and text to boost the bidirectional translation process, and we further extract inter-media and intra-media reward signals to provide complementary clues for learning cross-media correlation. Extensive experiments are conducted on cross-media retrieval to verify the effectiveness of our proposed RCBT approach, compared with 11 state-of-the-art methods on three cross-media datasets. [ABSTRACT FROM AUTHOR]

Due to the superior material properties, SiC mosfet is a promising candidate switching device for high power density and high efficiency power conversion system. The robustness of switching device under extreme temperature condition becomes a crucial factor to ensure power conversion system safely and continuously operating. In this paper, the temperature dependence of dynamic performance of 1.2-kV 4H-SiC power mosfets is systematically characterized over such wide temperature range of 90–493 K and compared with 1.2-kV Si IGBT by a layout optimized double pulse tester (DPT). The degradation of dynamic on-resistance related interface traps is analyzed specially and the energy loss caused by degradation is quantified at cryogenic temperatures. Besides, to validate the performance of SiC mosfet under safely and continuously operating conditions for cryogenic temperature application, a hard switched non-isolated dc–dc buck converter is designed and tested to estimate temperature dependence of conversion efficiency under temperature range of 90–290 K. Moreover, the further characterizations are conducted with gate resistance range of 2–20 Ω, load current range of 3–30 A, and converter output current of 5–22.5 A under different switching frequency (up to 150 kHz) to validate high power and high frequency application potential of SiC mosfet. [ABSTRACT FROM AUTHOR]

In this article, the static, dynamic, and short-circuit properties of 1.2-kV commercial 4H-SiC planar and trench gate metal–oxide–semiconductor field-effect transistors (MOSFETs) are compared and analyzed in a wide temperature range from 90 to 493 K. The temperature-dependent specific ON-resistance (${R}_{\text {sp}- \mathrm{\scriptscriptstyle ON}}$) and threshold voltage (${V}_{\text {th}}$) are analyzed in relation to the density of the interface state. The turn-on rise and turn-off fall times (${T}_{r}$ and ${T}_{f}$) and the corresponding energy loss (${E}_{r}$ and ${E}_{f}$) are extracted from a double-pulse test from cryogenic to high temperature and analyzed. The short-circuit capability of the two structures is studied at low temperature for the first time. The comprehensive comparison and analysis of the planar and trench gate MOSFET versus temperature in this work show the importance to study applications with SiC MOSFETs in a wide temperature range, especially for the cryogenic temperatures. [ABSTRACT FROM AUTHOR]

In this paper, an improved 4H-SiC U-shaped trench-gate metal–oxide–semiconductor field-effect transistors (UMOSFETs) structure with low ON-resistance (${R}_{ \mathrm{\scriptscriptstyle ON}}$) and switching energy loss is proposed. The novel structure features an added n-type region, which reduces ON-resistance of the device significantly while maintaining the breakdown voltage (${V}_{\textsf {BR}}$). In addition, the gate of the improved structure is designed as a p-n junction to reduce the switching energy loss. Simulations by Sentaurus TCAD are carried out to reveal the working mechanism of this improved structure. For the static performance, the ON-resistance and the figure of merit (FOM $= {V}_{\textsf {BR}}^{\textsf {2}}/{R}_{ \mathrm{\scriptscriptstyle ON}}$) of the optimized structure are improved by 40% and 44%, respectively, as compared to a conventional trench MOSFET without the added n-type region and modified gate. For the dynamic performance, the turn-on time (${T}_{ \mathrm{\scriptscriptstyle ON}}$) and turn-off time (${T}_{ \mathrm{\scriptscriptstyle OFF}}$) of the proposed structure are both shorter than that of the conventional structure, bringing a 43% and 30% reduction in turn-on energy loss and total switching energy loss (${E}_{\mathbf {SW}}$). [ABSTRACT FROM AUTHOR]

A novel post-oxidation annealing (POA) process with supercritical N2O (SCN2O) fluid is reported to be highly effective in improving the interface properties of the SiO2/4H–silicon carbide (SiC) (0001) systems. After SCN2O POA, the interface state density reduces to 2.8 × 1011 eV−1cm−2, which is about 3.5 times lower than that after a traditional high-temperature N2O POA process. Meanwhile, the highest oxide critical electric field shows an increase of 18.19% and the near-interfacial oxide traps is reduced by 69.90% compared with that after N2O POA process. The process temperature is as low as 120 °C. The significantly reduced processing temperature avoids additional defect generation while the supercritical state provides a stronger nitridation effect. SCN2O annealing is a promising candidate for POA process toward high-performance SiC power metal-oxide-semiconductor field effect transistors (MOSFETs). [ABSTRACT FROM AUTHOR]

Conventional switched-mode power supplies have intrinsic instantaneous power pulsation at the switching frequency thus require bulky filters. To improve the power density, this paper proposes a concept named the mixed analog–digital (MAD) which can be applied as dc–dc converters. By inserting an analog voltage component between the load and source, the output voltage naturally has much smaller fluctuation thereby much smaller passive filter is required. Simulations and experiments validate that the proposed MAD concept can be applied as dc–dc converters to significantly increase the power density. [ABSTRACT FROM AUTHOR]