ABSTRACTGrowing concerns about electromagnetic radiation from communication technologies such as 5G have prompted a search for effective microwave‐absorbing materials to mitigate potential health risks. This study focuses on the development of Fe‐based amorphous/nanocrystalline alloys as microwave absorbers, with specific emphasis on achieving cost‐effectiveness, reduced thickness, and superior absorption capabilities. FePC alloy powders, treated through thermal annealing and ball milling (synergistic processing), exhibit enhanced saturation magnetization and superior microwave absorption properties. The powders, with small particle sizes and high surface areas, demonstrate excellent absorption, achieving a minimum reflection loss (RL) of −30.1 dB at 12.8 GHz with a 5.3 GHz absorption bandwidth at 2 mm thickness. The results highlight the promising potential of these materials for practical applications in reducing electromagnetic interference, offering a combination of high performance, low cost, and easy processing.

ABSTRACTHeavy metal ions are toxic to the environment and the human body, so it is particularly important to develop sensor technology that can detect them quickly. However, the existence period of the preparation and testing device is long, the cost is high, and there is secondary pollution to the environment during the configuration of heavy metal solution. Therefore, the simulation calculation method can improve the efficiency and protect the environment by conducting preliminary research on the adsorption characteristics of materials and device characteristics. In this paper, AlGaN/GaN HEMT devices are used to detect heavy metal mercury ions by sensing the change of surface potential. First, based on density functional theory (DFT) and first principles, the change of material properties caused by the adsorption of mercury ions to the AlGaN surface is obtained. It is calculated that the band gap of the material decreases by 0.245 eV after the adsorption of one mercury ion. Then, the TCAD tool was used to construct the AlGaN/GaN HEMT device model, and the output current of the HEMT device was increased by 10.162 mA by correcting the characteristic parameters of the material so as to realize the detection of heavy metal mercury ions, providing reference for the preparation and development of heterostructure HEMT devices based on AlGaN/GaN.

ABSTRACTThe simulation of nanotransistors and the inclusion of all relevant physics is a challenging task, especially when working with one‐dimensional (1D) nanomaterials in which quantum confinement strongly influences the material properties and device performance. Several groups have already developed state‐of‐the‐art quantum transport simulators based on the first principles non‐equilibrium Green's function (NEGF) formalism, and a few have been commercialized. However, these tools are computationally demanding as they require solving the NEGF and the 3D Poisson equation. Here we present an open‐source quantum‐transport solver for the first principles device engineering for nanoelectronics (QUDEN) implemented in Matlab. QUDEN uses NEGF and the ballistic top‐of‐the‐barrier model to simulate ultrascaled field‐effect transistors (FETs) with channels made of nanoribbons of 2D materials, while the device Hamiltonian is obtained using first principles density functional theory (DFT) in combination with maximally localized Wannier functions (MLWFs). This approach preserves the accuracy of the full NEGF‐3D Poisson simulation in the on‐state while using a simplified self‐consistent electrostatics that leads to a much lower computational burden. Taking monolayer germanium‐selenide (GeSe) nanoribbons as an example, we show that QUDEN can be used for fast screening and accurate evaluation of numerous 2D/1D materials for future FETs.

ABSTRACTIn this paper, a mathematical model that describes the anomalous diffusion of a solvent in a spherical glassy polymer is studied. To solve this mathematical problem, which includes the time‐fractional diffusion equation with an inconstant diffusion coefficient and a nonlinear boundary condition, an iterative method based on the implicit finite difference method is presented. We prove the stability and convergence of the proposed numerical scheme. To show the capability and efficiency of the numerical method, the results obtained for different parameters with constant and inconstant diffusion coefficients are presented.

ABSTRACTThe performance of metal workpieces is crucial in industry, and the evaluation of their surface defects is a key factor in ensuring the safety of industrial production. Eddy Current Testing (ECT) offers a non‐contact, non‐invasive method for detecting metal surface defects efficiently and accurately. However, the evaluation of metal surface defects often heavily relies on the experience of operators, which is not only inefficient but also susceptible to human factors. To effectively address the above issues, this paper proposes an evaluation method using Deep Learning (DL) technology and designs a CSE‐LSTMNet model, which introduces the dilated convolutional layer on top of the traditional CNN‐LSTM model, enabling faster training speeds and higher accuracy. Meanwhile, a Squeeze and Excitation Networks (SENet) module is added after each residual block to enable the model to automatically acquire the significance of each feature channel, thus enhancing useful channel features while suppressing unimportant ones. The CSE‐LSTMNet model has been tested on the publicly available MDDEECT dataset. The simulation results show that the proposed CSE‐LSTMNet model exhibits excellent performance in the metal surface defect classification task. Compared with the traditional CNN‐LSTM benchmark model, the model achieves significant performance improvement, with the classification accuracy increasing from 86.67% to 92.78% and the F1 score increasing from 86.69% to 92.79%. This indicates that the CSE‐LSTMNet model has higher performance and significant advantages in metal defect evaluation.

ABSTRACTIn this article, performance analysis of gate‐all‐around negative capacitance vertical tunnel FET (GAA‐NC‐VTFET) is proposed. The device creates a heterojunction design with negative capacitance HfO2 (NC‐HfO2), and the presented structure is analyzed for the reduction of various short channel effects for minimal power applications. Because of the negative capacitance effects, the presented device shows steeper characteristics and is designed to lower the leakage current while maintaining a high ION/IOFF ratio. The polarization effect of negative capacitance has provided better outcomes in terms of a lower point subthreshold slope of 18 mV/dec and a leakage current of the order of 10−14 A/μm. An enhanced ION/IOFF ratio of around 1010 has been attained, having a length of channel of 18 nm. Since the IOFF value of the device is low, its power consumption is about μW, making it acceptable for low‐power applications.

ABSTRACTNiO/Ga2O3 heterojunctions are of significant interest due to their potential applications in power electronics and optoelectronics. Accurate extraction of capacitance‐voltage (C‐V) parameters is crucial for understanding their electrical characteristics and fundamental physical phenomena involved. In this context, this work investigates the temperature‐dependent C‐V characteristics of NiO/Ga2O3 heterojunction diodes (HJDs) before and after thermal annealing. The voltage barrier (VB) and the effective doping density (Neff) are extracted from these characteristics using the familiar analytical modeling (CAM) as well as an artificial intelligence (AI) based on particle swarm optimization (PSO) algorithm. Neff showed a decrease with increasing temperature, which is unusual behavior and is related to deep defects in Ga2O3. Traps revealed by deep‐level transient spectroscopy (DLTS) and Laplace‐DLTS (LDLTS) measurements were exploited to perform simulation using SCAPS. First, an ideal NiO/Ga2O3 HJD is considered, and then the defects of the fresh and annealed samples are considered. The results confirmed the influence of traps and exhibited consistent behavior with the observed pattern. The band diagram evolution with temperature has provided further insight into this phenomenon. Furthermore, PSO results were compared with those of CAM and demonstrated that the PSO algorithm offers superior accuracy in parameter extraction, as evidenced by lower root mean square error (RMSE) values, reaching a minimum of 4.65 × 10−13. This approach provides a better method for evaluating the extracted parameters from the C‐V characteristics of Ga2O3‐based heterojunction devices.

ABSTRACTIn order to simulate a laser system, the evaluation of a complex semilinear master equation is needed, including the description of the wave propagation by Maxwell's equations and appropriate rate equations. The denser the spatial discretization, the slower the computation time of the time‐dependent propagator. To counteract this restriction, the complexity is reduced by applying a nonuniform discretization and by using local operators, since it has already been shown that local operators are more efficient than a global operator. However, in practice, the main task is the management of the data transfer within the framework of local operators, as the necessary extraction and assembly processes represent an unintended overhead next to the actual expansion in terms of the Faber polynomial‐based approximation. Here, the use of a CPU and GPU is investigated to reduce the overhead through multiprocessing and to accelerate the expansion through multithreading, respectively. From the results obtained, the usefulness of the proposed approach is demonstrated. Also, an operator splitting approach onto the semilinear master equation is investigated to decrease the runtime, as the underlying matrix can be split into commutating matrices without splitting error.

ABSTRACTThe artificial neural networks (ANN) models have been proven to accurately describe the device's electrical behaviors. However, considering the importance of device physics, the physical‐based SPICE models are still the golden standard in the industry. This paper investigates two directions of ANN‐assisted SPICE modeling solutions. A scalable inductor model demonstrates the use of ANN to extract the scaling equation, and the hybrid model showcases the ANN enhancement on top of the physical SPICE model. The results show the potential feasibility and practicality of the ANN modeling approach to improve the SPICE model extraction process in speed and accuracy.