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This work investigates the off-state leakage characteristics of AlN/GaN/AlGaN high electron mobility transistors (HEMTs) on Si substrate with varying mesa depths and uncovers the existence of a parasitic channel associated with the AlGaN back barrier. Significant differences in off-state leakage up to three orders of magnitude were observed between devices fabricated using different mesa depths. The electrical properties of AlN/GaN/AlGaN HEMTs were measured, and it was found that there is a N-type parasitic channel in the unintentionally doped AlGaN back-barrier. Analysis of the isolation test structure, which retains this parasitic channel, reveals a buffer leakage of 12.8 mA/mm and a sheet resistance of 7739.1 Ω/sq, as a result of the parasitic channel. The depletion electric field strength of the parasitic channel is 3.2 × 105 V/cm. Temperature-dependent I–V curves obtained from the isolation area affirm that the primary leakage mechanism is two-dimensional variable range hopping along the sidewall. As the isolation distance extends from 3 to 5 μm, the slope of the fitting line decreases from −53.14 to −126.11 due to increased resistance. [ABSTRACT FROM AUTHOR]

In0.17Al0.83N/GaN high‐electron‐mobility transistor (HEMT) using GaN‐on‐Insulator (GaNOI) technology via 200 mm wafer bonding technique is developed with good DC and RF performance and high fT/fmax. Measurements obtained from X‐Ray diffraction and micro‐Raman spectroscopy have demonstrated a 5% reduction in “a lattice strain,” which results in the improvement of the sheet resistance (Rsh) from 301 to 284 Ω □−1. A 120 nm gate‐length device achieves a peak fT up to 96 GHz which yields a fT × Lg value of 11.5 GHz μm, which compares favorably with reported GaN‐based HEMTs on Si. These results demonstrate that GaNOI HEMT on Si is an attractive candidate for future mm‐wave applications. The implementation of GaNOI technology facilitates the integration of GaN devices into a chip alongside complementary metal–oxide–semiconductor technology that opens up the potential for integrated high‐power and RF applications, enabling more compact and efficient systems. [ABSTRACT FROM AUTHOR]

Aero-space aluminum alloys, as vital materials in aerospace engineering, find extensive application in various aerospace components. However, prolonged usage often leads to the emergence of fatigue natural cracks, posing significant safety risks. Therefore, research on accurate quantitative detection techniques for the cracks in aerospace-aluminum alloys is of vital importance. Firstly, based on the three-points bending experimental model, this paper prepared the fatigue natural crack specimen, and the depth of the natural crack is calibrated. Then, given the complexity of geometric characteristics inherent in natural cracks, the pulsed eddy current signal under the different natural crack depth is acquired and analyzed using an experimental study. Finally, to better exhibit the non-linearity between PEC signal and crack depth, a GA-based BPNN algorithm is proposed. The Latin Hypercube method is considered to optimize the population distribution in the genetic algorithm. The results indicate that the characterization accuracy reaches 2.19% for the natural crack. [ABSTRACT FROM AUTHOR]

Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature’s unsupervised approaches.

The main factors restricting the mining speed of the working face in Yuenan Coal Mine are the water-rich area of limestone above the coal seam and the internal geological concealed structure. The transparent detection of the working face is carried out by using transient electromagnetic method, radio wave perspective and slot wave seismic geophysical methods. In the process of detection, it is found that the induced voltage received by the secondary wave field of transient electromagnetic method is more sensitive to the area of rich water and low resistance, and the lateral situation of coal seam can be identified by observing the energy attenuation emitted and received by radio wave perspective. The integrated analysis of envelope amplitude, spectrum and dispersion curve of in-seam wave seismic imaging has good effect on identifying fault strike. Through the identification of the comprehensive response characteristics of the abnormal region by the induction factor in the multi-wave field, the plane map is superimposed according to the abnormal area delineated by the multi-source wave field, and a variety of abnormal characteristics are shown synthetically, which can effectively identify the correlation between the water-rich area of the roof of the working face and the internal structural area of the coal seam, and clearly judge the existence of the water channel. The test results show that the geophysical method can find out the internal abnormal area of the mining face and predict in advance, and the combined detection of various geophysical methods can provide a reference basis for similar geological conditions in the future. [ABSTRACT FROM AUTHOR]