International Journal of Numerical Modelling-Electronic Networks Devices and Fields最新文献

Reconfigurable reflectarray antenna (RRA) has been considered a potential technology for future communication. However, few studies focus on investigating the intermodulation distortion induced by its active devices, which are employed to provide different compensation phases for the RRA elements. In this paper, the radiation pattern prediction for third-order intermodulation (IM3) distortion of the RRA with embedded varactor diodes is first proposed. It is implemented with a nonlinear model of the varactor and full-wave electromagnetism (FEM) simulation. The presented varactor model is first employed to design the RRA and then to acquire the power and phase of the IM3 signal in each element of the RRA, which is realized with a nonlinear harmonic-balance and FEM co-simulation. With the help of the HFSS software, the radiation pattern is achieved when the co-simulation results of the RRA elements are input. The IM3 radiation measurement of the RRA is presented to verify the radiation pattern prediction of the IM3 signal. The consistent comparison results between the simulation and the measurement indicate that the proposed method is accurate. Significantly, the radiation pattern of the IM3 signal is different from that of the fundamental signal based on the designed RRA. It is meaningful and implies that it may have linearization approaches for the RRA. The proposed method will enhance the application of the RRA since the linearization approach is still a scientific challenge in future wireless networks.

In addressing the pattern synthesis of multiconstraint thinned planar antenna arrays, existing intelligent optimization algorithms encounter limitations such as premature convergence and insufficient solution accuracy. Therefore, we propose an adaptive mutation strategy dung beetle optimizer (AMSDBO) for optimizing thinned planar antenna arrays. The AMSDBO algorithm innovatively constructs a three-stage collaborative optimization framework, effectively achieving high-performance design for thinned planar arrays under the constraints of fixed aperture size and sparsity rate through a parameter adaptive adjustment mechanism. Firstly, initial solutions for the dung beetle populations are generated using a chaotic mapping reverse learning (CMRL) strategy to enhance both population diversity and the quality of the initial solutions. Next, the local mutation search (LMS) mechanism is introduced. An adaptive T-distribution mechanism is imposed to effectively enhance the early global search capability. Then, the Lévy flight variation strategy is employed to adaptively adjust the positions of the dung beetle population in the later stages, helping the algorithm avoid local optima and accelerating convergence. Simulation results with classical test functions demonstrate that AMSDBO offers significant advantages in convergence accuracy and robustness compared to traditional algorithms (DBO, PSO, and IWO). Additionally, two sets of typical planar thinned array experimental results indicate that the optimization performance of the AMSDBO algorithm is significantly improved compared to traditional optimization algorithms. Specifically, there is a peak sidelobe level (PSLL) reduction of 15.5% for DBO, 11.64% for PSO, and 14.56% for IWO. This confirms the effectiveness and superiority of the AMSDBO algorithm.

Relatively few in-depth studies on MOSFET mobility are published at cryogenic temperatures partially due to lack of appropriate extraction method associated with more complicated scattering mechanisms than at room temperature. This paper, for the first time, proposes a new Y-function method, which is both physically and engineering novel, for mobility extraction considering the Coulomb scattering that dominates toward cryogenic temperatures. This new Y-function method demonstrates excellent fit with measurement data taken from foundry fabricated 180 nm bulk MOSFETs for the temperature range from 300 down to 4 K.

Analytical models of base transit time $��\left({\tau }_B\right)�$ for SiGe-HBTs usually neglected carrier recombination in the base under the assumption of very thin base. The validity of this assumption is questionable under intermediate injection level (IIL) condition, which is common for highly-scaled devices operating in the high-current regime. However, consideration of recombination in the base along with various nonideal physical models reported in the literature under IIL condition leads to the analytical intractability of the governing differential equation (GDE). Therefore, this work intends to develop an analytically tractable $��{\tau }_B�$- model of SiGe-HBTs applicable under IIL conditions. The model also considers the effects of base width modulation (BWM) on $��{\tau }_B�$ to simulate the effects of the base pushout phenomena usually occurred at high-current regime. Close match of the simulated model data and experimentally measured data for the collector current density, total transit time, unity gain-bandwidth cutoff frequency and maximum frequency of oscillation validates the model quite well. It is noteworthy that significant deviation from the measured data has been observed for the model that does not consider recombination at high-current regime. Therefore, the proposed model not only provides the justification of the consideration of the effects of carrier recombination in the base to develop analytical $��{\tau }_B�$ model but also shows the practical significance of the developed model to guide the device engineers to design modern highly-scaled SiGe-based HBT devices operating in the high current regime, thereby meeting the requirement of sustainable industrialization to facilitate sustainable development goal 9 (SDG 9).

The domestic induction heating system comprises a heating coil and electromagnetic media (ferromagnetic cookware and a magnetic shielding layer). Horizontal or vertical misalignments between the heating coil and cookware during operation inevitably alter the coil's equivalent self-inductance. This variation in self-inductance affects impedance matching and complicates power regulation. Currently, calculations of this parameter rely mainly on finite element analysis (FEA) methods. However, FEA methods require significant computational time and may encounter convergence issues due to poor mesh quality, especially when simulating thin layers with large aspect ratios, where stringent meshing requirements often lead to non-convergence near the surface. This work develops an extended analytical model that incorporates the finite size of the magnetic shielding layer and the finite conductivity of cookware materials (aluminum, 304 stainless steel, and 430 stainless steel). The proposed model accounts for variations in coil dimensions, the relative permeability of the magnetic shielding layer, and the conductivity of cookware materials. It achieves a computational speed five times faster than FEA simulations while maintaining an error of less than 5% compared to experimental results.

This paper mainly designs the specific optimization process of antenna parameters or topology structure based on the online updated multi-layer perceptron (MLP) agent model, and introduces principal component analysis (PCA) technology to accelerate the overall optimization process. In addition, to address the problem of mixed optimization of machine learning assisted antenna parameters and topology structure, this paper proposes a new encoding approach, which enables two forms of variables to jointly participate in antenna optimization. The specific approach is to treat the topology structure and antenna parameters as a state and encode them in binary, thereby unifying them into a set of binary variables, which facilitates the introduction of subsequent machine learning and optimization algorithms. By optimizing an ultra-wideband G-patch monopole antenna and a microstrip pixel antenna, it has been demonstrated that the proposed method has better optimization performance compared to traditional optimization methods and can be applied to antenna parameters or topology structures.

This study introduces a new formulation of the Wave Concept Iterative Process (WCIP) method in elliptic coordinates to analyze electromagnetic scattering from an infinitely long, inhomogeneous, multilayered elliptical cylinder. The foundation of this approach lies in the definition of emitted and reflected waves at the interfaces of dissimilar media, utilizing the tangential components of the electric field and current density. The unknown waves resolved from a set of two equations. The first equation, formulated in the spectral domain, characterizes the response of the environment around the structure. The second equation guarantees the continuity of the electromagnetic fields and enforces boundary conditions at the interfaces that separate different regions. The system of equations is resolved by an iterative process where a discrete angular Mathieu Transformation Algorithm is introduced to toggling between the spatial and the modal domain. The proposed approach is suitably implemented in a specific computational code to achieve a numerical solution. Some numerical results are presented, involving a structure composed of metallic strips positioned at the interfaces of four elliptical dielectric layers, which is illuminated by a transverse magnetic (TM) plane wave at an oblique angle of incidence. The result is in agreement with those published in the literature.

This article presents a comprehensive temperature-dependent analysis of Ferroelectric Junction-less Surrounding-Gate FETs (Fe-JL-SG FETs). To evaluate the effectiveness of ferroelectric integration, a detailed comparison between Fe-JL-SG FETs and conventional Junction-less Surrounding-Gate FETs (JL-SG FETs) is carried out. Key device parameters—including surface potential, electric field, electron concentration, electron velocity, transconductance (g_m), output conductance (g_d), capacitance (C_GG), cutoff frequency (f_t), and I_ON/I_OFF ratio—are systematically investigated over a broad temperature range (100–500 K). The results demonstrate that Fe-JL-SG FETs exhibit superior electrical and analog performance with significantly reduced sensitivity to temperature variations compared to JL-SG FETs. This enhanced behavior is attributed to the negative capacitance effect introduced by the ferroelectric material, which effectively reduces the subthreshold-swing (SS) below the Boltzmann limit (60 mV/decade). However, this effect weakens as temperature increases. The simulation framework, developed using the ATLAS 3-D device simulator, exhibits strong congruence with the proposed analytical model, thereby validating the theoretical constructs and reinforcing the predictive reliability of the developed approach.

This research presents the design and optimization of a miniaturized frequency-selective surface (FSS)-embedded fractal MIMO antenna for 28 GHz 5G New Radio (NR) applications. The compact antenna, measuring 4.2 × 1.87 λ, employs a 7 × 3 unit-cell array to achieve superior radiation characteristics. The design methodology integrates fractal geometry analysis, FSS loading for electromagnetic enhancement, and machine learning-based optimization to achieve optimal gain, impedance bandwidth, and radiation efficiency. The antenna, realized on a Rogers RT5880 substrate, is optimized using Random Forest (RF), Artificial Neural Network (ANN), and regression-based predictive models. Performance evaluation using R², MAE, and MSE confirms the robustness of the optimization framework. Key design challenges include maintaining high isolation among compact MIMO elements, ensuring stable broadband response, and balancing miniaturization with gain without sacrificing efficiency. These challenges are addressed through precise geometric refinement, effective FSS coupling, and iterative machine learning tuning. Experimental validation using a ZVA 67 vector network analyzer demonstrates a broad impedance bandwidth of 2200 MHz (26–28.2 GHz), a peak gain of 7.53 dBi, and a radiation efficiency of 88.49%. The FSS integration enhances isolation, suppresses surface waves, and improves overall mm Wave performance. The proposed ML-assisted FSS-MIMO design provides an efficient solution for next-generation 5G wireless communication systems.