Power converters based on gallium nitride (GaN) are progressing swiftly owing to their exceptional efficiency and tiny dimensions, boosted by high power density and fast switching capabilities. Nevertheless, these benefits are accompanied by considerable thermal management issues that impact reliability, performance, and operational lifespan. This review examines advanced thermal management approaches for high-power-density GaN power converters, including active and passive cooling technologies, sophisticated packaging designs, and the use of novel materials like graphene and diamond to improve heat dissipation. The impacts of thermal boundary resistance, self-heating phenomena, and substrate selection on thermal performance are thoroughly analyzed. Strategies for enhancing printed circuit board (PCB) layouts, thermal vias, and the use of thermal interface materials (TIMs) are also emphasized. The study highlights co-design approaches that optimize thermal resistance and layout efficiency, supporting GaN operation under high-frequency conditions. This thorough investigation offers insights into addressing the thermal challenges linked to GaN technology, promoting its adoption in forthcoming power devices.

This paper presents a novel technique for modeling the electrostatic discharge snapback phenomenon in integrated circuits (ICs). The macromodel is built using standard components: BSIM3v3.2 model for the MOSFET, a bipolar transistor modeled by Mextram 504.7, and a substrate resistor. The IC under test is characterized by its die and package impedance. The model should allow easier simulation program with IC emphasis implementation, high simulation speed, less convergence issues, and wider availability of a gate-grounded n-type MOSFET protection device. Our model determines the interaction between the protection device and the internal circuitry of the IC. The model parameters are extracted with MATLAB script. Simulation results are compared with transmission-line pulsing measurement for a voltage regulator NCV4949 and a controller area network transceiver TLE6250G.

In this paper, an intelligent technique for Frequency Selective Surfaces (FSS) synthesis for 5G shielding applications is presented. Our approach applies a Neural Network (NN) architecture trained on a dataset of FSS electromagnetic (EM) responses to predict directly optimal geometrical configurations from desired shielding specifications. The proposed solution is a very useful tool to offer radiofrequency (RF) designers a smart service to optimize RF system design considering the harmful EM environment of the device under test. The developed solution is mainly based on multivalued inverse machine learning (MIML) method. It was optimized so that it takes account of the effect of polarization and incidence angles of incident EM waves and makes the proper arrangement of the shielding FSS screen, relying on the desired frequency behavior. We demonstrate the efficiency of our proposed approach through comprehensive simulations and experimental validations. Results indicate that the proposed NN-empowered inverse FSS design proposed method achieves superior performance in terms of shielding effectiveness (SE) and design efficiency compared to traditional direct machine learning (ML) modeling approaches.

This paper presents a comparative study of various artificial intelligence methods, including artificial neural networks (ANNs), recurrent neural networks (RNNs), k-nearest neighbors (KNN), random forests (RFs), and particle swarm optimization (PSO) techniques, to see which one can predict conducted electromagnetic interference (CEMI) better. The DC/DC converter simulations and experimental results demonstrated a high level of matching. According to the simulation results, the datasets were highlighted by varying key parameters related to the supply voltage, load current, switching frequency, duty cycle, component choice, PCB layout, filter capacitance, and gate resistance in a systematic way. During the assessment, each AI technique is checked regarding prediction accuracy, computational efficiency, and error rates using different metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). It is observed that KNN performs better than the other methods, giving only the lowest error in predictions and showing very fast computing speed. Furthermore, KNN gave the best results with R² above 0.97, MAE below 5.9 dBµV, and RMSE under 7.3 dBµV. This method worked better than others in all test cases. According to the measurements, the predicted and actual EMI levels match very well and show that the proposed method is strong and reliable. Further, basically, these results show that KNN has the same potential to work as an effective and efficient tool for predicting CEMI in power electronics. Its strong performance can further help in developing better and more reliable power systems for practical use, while the system itself provides valuable insights to engineers for electromagnetic compatibility design and compliance.

Ambient Backscatter Communication (AmBC) represents a recent breakthrough in wireless communication technology. It enables devices to communicate wirelessly using ambient radio frequency (RF) signals rather than generating their own carriers. This advancement holds considerable promise for Internet of Things (IoT) applications, particularly in addressing the challenge of power efficiency. In this paper, we introduce, for the first time in literature, a novel prototype of a backscatter modulator tag that works in the ambient 5G-NR signal (3.5 GHz). An experimental functional validation of the 5G-NR backscatter tag is presented by using BPSK modulation. Additionally, we have explored the prototype feasibility focusing mainly on the differentiation of power levels between the direct link and the backscattered signal, highlighting a critical challenge in 5G-NR backscatter communication.