Search Results (539)

A super-polishing procedure was performed on the Al₂O₃ (0001) surface for epitaxial purposes. The roughness of the final polished surface was measured to be 0.16 nm using atomic force microscopy and X-ray reflectivity techniques. After heat treatment at 130 °C, results from low-energy electron diffraction and Auger energy spectroscopy indicated that the top surface was well ordered and clean, rendering it suitable for epitaxial growth. The successful growth of a GaN thin film on an Al₂O₃ (0001) substrate was confirmed by the hk-circle scan in XRD and the presence of a sharp peak in the rocking curve of the GaN (0002) Bragg peak. These findings indicate that the top surface of the substrate is conducive to epitaxial growth. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

Gallium nitride (GaN) enhancement-mode high-electron-mobility transistors ( E-HEMTs) deliver superior performance compared to traditional silicon (Si) and silicon carbide (SiC) counterparts. Their faster switching speeds, lower on-state resistances, and higher operating frequencies enable more efficient and compact power converters. However, their integration into high-power applications is limited by critical reliability concerns, particularly regarding their short-circuit (SC) withstand capability and overvoltage (OV) resilience. GaN devices typically exhibit SC withstand times of only a few hundred nanoseconds, needing ultrafast protection circuits, which conventional desaturation (DESAT) methods cannot adequately provide. Furthermore, their high switching transients increase the risk of false activation events. The lack of avalanche capability and the dynamic nature of GaN breakdown voltage exacerbate issues related to OV stress during fault conditions. Although SC-related behaviour in GaN devices has been previously studied, a focused and comprehensive review of protection strategies tailored to GaN technology remains lacking. This paper fills that gap by providing an in-depth analysis of SC and OV failure phenomena, coupled with a critical evaluation of current and next-generation protection schemes suitable for GaN-based high-power converters. Full article

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article

In this study, we introduce a high-resolution, high-speed thermal imaging technique using Raman spectroscopy to simultaneously measure the temperature of a substrate and a channel. By modifying the Raman spectrometer, we achieved a measurement speed faster than commercial spectrometers. This system demonstrated a sub-micron spatial resolution and the ability to measure the temperatures of the Si substrate and GaN channel simultaneously. During high-current operation, we observed significant self-heating in the GaN channel, with hotspots 100 °C higher than the surroundings, while the Si substrate showed an even temperature distribution. The ability to detect hotspots can help secure the reliability of devices through early failure analysis and can also be used for improvement research to reduce hotspots. These findings highlight the potential of this technique for early defect inspection and device improvement research. This study provides a novel and effective method for measuring the sub-micron resolution temperature distribution in devices, which can be applied to various semiconductor devices, including SiC-based power devices. Full article

Background: Limited availability of high-quality labeled biomedical image datasets presents a significant challenge for training deep learning models in medical diagnostics. This study proposes a novel image generation framework combining conditional generative adversarial networks (cGANs) with a Mixture-of-Experts (MoE) architecture and color histogram-aware loss functions to enhance synthetic blood cell image quality. Methods: RGB microscopic images from the BloodMNIST dataset (eight blood cell types, resolution 3 × 128 × 128) underwent preprocessing with k-means clustering to extract the dominant colors and UMAP for visualizing class similarity. Spearman correlation-based distance matrices were used to evaluate the discriminative power of each RGB channel. A MoE–cGAN architecture was developed with residual blocks and LeakyReLU activations. Expert generators were conditioned on cell type, and the generator’s loss was augmented with a Wasserstein distance-based term comparing red and green channel histograms, which were found most relevant for class separation. Results: The red and green channels contributed most to class discrimination; the blue channel had minimal impact. The proposed model achieved 0.97 classification accuracy on generated images (ResNet50), with 0.96 precision, 0.97 recall, and a 0.96 F1-score. The best Fréchet Inception Distance (FID) was 52.1. Misclassifications occurred mainly among visually similar cell types. Conclusions: Integrating histogram alignment into the MoE–cGAN training significantly improves the realism and class-specific variability of synthetic images, supporting robust model development under data scarcity in hematological imaging. Full article

This research takes the Suzhou Gardens, a World Cultural Heritage Site, as the object of study and investigates a rapid scheme generation approach for garden restoration and expansion projects, assisting designers in making scientific decisions. Considering the limitations of current garden design, which is inefficient and relies on human experience, this study proposes an intelligent generation framework based on a conditional generative adversarial network (CGAN). In constructing the CGAN model, we determine the spatial characteristics of the Suzhou Gardens and, combined with historical floor plan data, train the network. We then design an optimization strategy for the model training process and finally test and verify the generative space scheme. The research results indicate the following: (1) The CGAN model can effectively capture the key elements of the garden space and generate a planar scheme that conforms to the traditional space with an accuracy rate reaching 91.08%. (2) This model can be applied to projects ranging from 200 to 1000 square meters. The generated results can provide multiple scheme comparisons for update decisions, helping managers to efficiently select the optimal solution. (3) Decision-makers can conduct space utilization analyses and evaluations based on the generated results. In conclusion, this study can help decision-makers to efficiently generate and evaluate the feasibility of different design schemes, providing intelligent support for decision-making in urban renewal plans. Full article

Automated font generation for complex, compositional scripts like Korean Hangul presents a significant challenge due to the 11,172 characters and their complicated component-based structure. While existing component-based methods for font image generation acknowledge the compositional nature of Hangul, they often fail to explicitly leverage the crucial positional semantics of its basic elements as initial, middle, and final components, known as Jamo. This oversight can lead to structural inconsistencies and artifacts in the generated glyphs. This paper introduces a novel two-stage framework that directly addresses this gap by imposing a strong, linguistically informed structural principle on the font image generation process. In the first stage, we employ a You Only Look Once version 8 for Segmentation (YOLOv8-Seg) model, a state-of-the-art instance segmentation network, to decompose Hangul characters into their basic components. Notably, this process generates a dataset of position-aware semantic components, categorizing each jamo according to its structural role within the syllabic block. In the second stage, a conditional Generative Adversarial Network (cGAN) is explicitly conditioned on these extracted positional components to perform style transfer with high structural information. The generator learns to synthesize a character’s appearance by referencing the style of the target components while preserving the content structure of a source character. Our model achieves state-of-the-art performance, reducing L1 loss to 0.2991 and improving the Structural Similarity Index (SSIM) to 0.9798, quantitatively outperforming existing methods like MX-Font and CKFont. This position-guided approach demonstrates significant quantitative and qualitative improvements over existing methods in structured script generation, offering enhanced control over glyph structure and a promising approach for generating font images for other complex, structured scripts. Full article

Forest fire detection is vital for ecological conservation and disaster management. Existing visual detection methods exhibit instability in smoke-obscured or illumination-variable environments. Although multimodal fusion has demonstrated potential, effectively resolving inconsistencies in smoke features across diverse modalities remains a significant challenge. This issue stems from the inherent ambiguity between regions characterized by high temperatures in infrared imagery and those with elevated brightness levels in visible-light imaging systems. In this paper, we propose MCDet, an RGB-T forest fire detection framework incorporating target-aware fusion. To alleviate feature cross-modal ambiguity, we design a Multidimensional Representation Collaborative Fusion module (MRCF), which constructs global feature interactions via a state-space model and enhances local detail perception through deformable convolution. Then, a content-guided attention network (CGAN) is introduced to aggregate multidimensional features by dynamic gating mechanism. Building upon this foundation, the integration of WIoU further suppresses vegetation occlusion and illumination interference on a holistic level, thereby reducing the false detection rate. Evaluated on three forest fire datasets and one pedestrian dataset, MCDet achieves a mean detection accuracy of 77.5%, surpassing advanced methods. This performance makes MCDet a practical solution to enhance early warning system reliability. Full article

Advanced persistent threat (APT) attacks present significant challenges to cybersecurity due to their covert nature, high complexity, and ability to operate across multiple temporal and spatial scales. Existing detection techniques often struggle with issues like class imbalance, insufficient feature extraction, and the inability to capture complex attack dependencies. To address these limitations, we propose a dual-phase framework for APT detection, combining multi-feature-conditioned generative adversarial networks (MF-CGANs) for data reconstruction and a multi-scale convolution and channel attention-enhanced graph convolutional network (MC-GCN) for improved attack detection. The MF-CGAN model generates minority-class samples to resolve the class imbalance problem, while MC-GCN leverages advanced feature extraction and graph convolution to better model the intricate relationships within network traffic data. Experimental results show that the proposed framework achieves significant improvements over baseline models. Specifically, MC-GCN outperforms traditional CNN-based IDS models, with accuracy, precision, recall, and F1-score improvements ranging from 0.47% to 13.41%. The MC-GCN model achieves an accuracy of 99.87%, surpassing CNN (86.46%) and GCN (99.24%), while also exhibiting high precision (99.87%) and recall (99.88%). These results highlight the proposed model’s superior ability to handle class imbalance and capture complex attack behaviors, establishing it as a leading approach for APT detection. Full article

High efficiency and high power density are key targets in modern power conversion. Operating power converters at high switching frequencies enables the use of smaller passive components, which, in turn, facilitate achieving high power density. However, the concurrent increase in switching frequency and power density leads to efficiency and overheating issues. Soft switching techniques are typically employed to minimize switching losses and significantly improve efficiency by reducing power losses. However, the hysteresis behavior of the power electronics devices’ output capacitance, C_OSS, is the cause of regrettable losses in Super-Junction (SJ) MOSFETs, SiC MOSFETs, and GaN HEMTs, which are usually adopted in soft switching-based conversion schemes. This paper reviews the techniques for measuring hysteresis traces and power losses, as well as the understanding of the phenomenon to identify current research trends and open problems. A few studies have reported that GaN HEMTs tend to exhibit the lowest hysteresis losses, while Si superjunction (SJ) MOSFETs often show the highest. However, this conclusion cannot be generalized by comparing the results from different works because they are typically made across devices with different (when the information is reported) breakdown voltages, on-state resistances, die sizes, and test conditions. Moreover, some recent investigations using advanced TCAD simulations have demonstrated that newer Si-SJ MOSFETs employing trench-filling epitaxial growth can achieve significantly reduced hysteresis losses. Similarly, while multiple studies confirm that hysteresis losses increase with increasing dv/dt and decreasing temperature, the extent of this dependence varies significantly with device structure and test methodology. This difficulty in obtaining a general conclusion is due to the lack of proper figures of merit that account for hysteresis losses, making it problematic to evaluate the suitability of different devices in resonant converters. This problem highlights the primary current challenge, which is the development of a standard and automated method for characterizing C_OSS hysteresis. Consequently, significant research effort must be invested in addressing this main challenge and the other challenges described in this study to enable power electronics researchers and practitioners to develop resonant converters properly. Full article

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

We consider the imbalanced data problem involving a new class of resampling-based models for classification. These models are variants of the conditional generative adversarial networks. An entropy regularization approach (RegCGAN) is employed to implement the corresponding imbalanced data learning. Its basic framework is introduced. Theoretical and simulation-based analyses are performed to demonstrate the existence and uniqueness of RegCGAN’s equilibrium point, and RegCGAN has excellent minority class prediction ability. We apply the results to two synthetically constructed and a real imbalanced dataset. Full article

Existing sonar target detection methods suffer from low efficiency and accuracy due to sparse target features and significant noise interference in sonar images. To address this, we introduce SFE-YOLO, an improved model based on YOLOv11. We replace the original detection head with an FSAFFHead module that enables adaptive spatial feature fusion. An EEA module is designed to direct the model’s attention to the intrinsic contour information of targets. We also enhance SC_Conv convolution and integrate it into C3K2 to improve detection stability and reduce information redundancy. Additionally, Focaler-IOU is introduced to boost the accuracy of multi-category target bounding box regression. Lastly, we employ a hybrid training strategy that combines pre-training with ADA-StyleGAN3-generated data and transfer learning with real data to alleviate the problem of insufficient training samples. The experiments show that, compared to the baseline YOLOv11n, the improved model’s precision and recall increase to 92% and 90.3%, respectively, and mAP50 rises by 12.7 percentage points, highlighting the effectiveness of the SFE-YOLO network and its transfer learning strategy in tackling the challenges of sparse small target features and strong noise interference in sonar images. Full article

We proposed a GAN-based method to generate a ship hull form. Unlike mathematical hull forms that require geometrical parameters to generate ship hull forms, the proposed method requires desirable ship performance parameters, i.e., the drag coefficient and tonnage. The objective of this study is to demonstrate the feasibility of generating hull geometries directly from performance specifications, without relying on explicit geometrical inputs. To achieve this, we implemented a conditional Wasserstein GAN with gradient penalty (cWGAN-GP) framework. The generator learns to synthesize hull geometries conditioned on target performance values, while the discriminator is trained to distinguish real hull forms from generated ones. The GAN model was trained using a ship hull form dataset generated using the generalized Wigley hull form. The proposed method was evaluated through numerical experiments and successfully generated ship data with small errors: less than 0.08 in mean average percentage error. Full article