Commercial Schottky-gate AlGaN/GaN high-electron-mobility transistors (HEMTs) exhibit a large peak in low-frequency (LF) noise magnitude at $\sim $ 325 K. An activation energy of 0.56 ± 0.05 eV is determined for the responsible generation–recombination (G–R) center via Arrhenius analysis. Comparisons with first-principles calculations and complementary experimental studies show that this G–R center is due to substitutional iron impurities, FeGa. Independent determination of the activation energy for this prominent noise peak enables recalibration of the Dutta–Horn model of LF noise for GaN-based HEMTs and a downward revision by $\sim $ 20% of past LF-noise estimates of effective defect energies. The resulting LF-noise-derived defect-energy distributions are consistent with deep-level-transient spectroscopy (DLTS) measurements on GaN-based diodes before and after 1.8-MeV proton irradiation. The activation of FeGa-related centers via defect dehydrogenation during high-voltage stress is demonstrated via LF noise measurements on AlGaN/GaN-on-SiC HEMTs.

Quadrotor unmanned aerial vehicles (UAVs) have emerged as ubiquitous and agile robots and data carriers within the framework of the future Internet of Things (IoT) and mobile wireless networks. Yet, the insufficient onboard battery necessitates the optimization of energy consumption for both the UAV and IoT devices while ensuring that communication requirements are met. This paper therefore proposes a more accurate and mathematically tractable model for characterizing a UAV’s energy consumption concerning desired trajectories. This nonlinear model takes into account the UAV’s dynamics, brushless direct current (BLDC) motor dynamics, and aerodynamics. To optimize the communication time between IoT devices and the UAV, IoT devices are clustered using a modified GAK-means algorithm, with dynamically optimized communication coverage radii. Subsequently, a fly–circle–communicate (FCC) trajectory design algorithm is introduced and derived to conserve energy and save mission time. Under the FCC approach, the UAV sequentially visits the cluster centers and performs circular flight and communication. Transitions between cluster centers are smoothed via 3D Dubins curves, which provide physically achievable trajectories. Comprehensive numerical studies indicate that the proposed trajectory planning method reduces overall communication time and preserves UAV battery energy compared to other benchmark schemes.

Total-ionizing-dose (TID) effects are evaluated in back-gated IGZO thin-film transistors irradiated under different gate biases. Negative-bias irradiation leads to worst-case degradation of TID response in these devices, primarily as a result of enhanced charge trapping in the SiO ₂ overlayer. The relatively small peak transconductance decrease after irradiation illustrates that IGZO transistors are much less sensitive to interface-trap buildup and other instabilities due to hydrogen release and transport than amorphous Si thin film transistors examined previously. The TID response of devices with different gate sizes is also investigated. No significant geometry dependence is observed, which is promising for future scaling down of the technology.

Donor-like defects are activated and acceptor-like defects are passivated when commercial AlGaN/GaN HEMTs are irradiated with 10-keV X-rays or 1.8-MeV protons at low fluence. Displacement-damage-induced creation of acceptor-like defects is observed at higher proton fluences. The dehydrogenation of FeGa-H and ON-H substitutional impurities and generation of N-vacancy-related defects most likely account for the modest degradation of these devices at high proton fluences. Low-frequency noise measurements identify FeGa defects as prominent generation-recombination centers in these devices. These results enable recalibration of the Dutta-Horn model of low-frequency noise and increased insight into the defect identities and energy distributions in AlGaN/GaN HEMTs.

Unsupervised feature selection (UFS) is an important technology for dimensionality reduction and has gained great interest in a wide range of fields. Recently, most popular methods are spectral-based which frequently use adaptive graph constraints to promote performance. However, no literature has considered the grouping characteristic of the data features, which is the most basic and important characteristic for arbitrary data. In this paper, based on the spectral analysis method, we first simulate the data feature grouping characteristic. Then, the similarity between data is adaptively reconstructed through the similarity between groups, which can explore the more fine-grained relationship between data than the previous adaptive graph methods. In order to achieve the aforementioned goal, the local similarity matrix and the global similarity matrix are defined, and the weighted KL entropy is used to constrain the relationship between the global similarity matrix and the local similarity matrices. Furthermore, the symmetrical self-representation structure is used to improve the performance of the reconstruction error term in the conventional spectral-based methods. After the model is constructed, a simple but efficient algorithm is proposed to solve the full model. Extensive experiments on 8 benchmark dataset with different types to show the effectiveness of the proposed method. The source code is available at: https://github.com/misteru/SSDS .

Total-ionizing-dose effects are evaluated in vertical-charge-trapping NAND devices with silicon oxynitride (SiON) and SiO2 tunneling layers. Processing splits include SiON tunneling layers with and without H2/D2 high-pressure annealing. Programmed devices were irradiated to 500 krad(SiO2) with 10-keV X-rays and annealed for 30 min. A second programming after annealing does not fully restore the original, programmed state. Radiation-induced trapped holes compensate deeply trapped electrons that are injected into the dielectric during device programming. Throughout the full irradiation and annealing sequence, threshold voltages remain large enough to enable successful NV memory application.