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

In this article, a genetic algorithm based on coarse and fine encoding is proposed to assist the parameter extraction method of the coplanar waveguide model. First, an equivalent circuit model is proposed to accurately characterize the electrical characteristic parameters of the coplanar waveguide. The proposed quasi-empirical equivalent circuit model not only has a certain physical meaning but can also realize the solution of the nonlinear relationship between the device performance parameters and the dimensional structure parameters. Then, a single-step genetic algorithm is proposed to accelerate the parameter extraction based on the proposed semi-empirical model of the coplanar waveguide. On this basis, a coarse and fine encoding genetic algorithm is proposed to accelerate the parameter extraction. The proposed parameter extraction method not only avoids the problem of inaccurate element values that may be caused by artificially determining partial parameter values, but also omits the process of solving simultaneous equations. It can also avoid the problem of insufficient solution accuracy caused by the large order-of-magnitude difference between the values of equivalent circuit elements. Therefore, the quasi-empirical equivalent circuit model and the parameter extraction method accelerated by the coarse and fine encoding genetic algorithm proposed can achieve accurate and efficient modeling of devices.

Compared to traditional technology computer-aided design (TCAD) simulations, using neural networks to predict semiconductor device performance does not face convergence problems. This advantage is particularly significant when simulating devices made of materials like silicon carbide (SiC), which exhibit complex physical behaviors, making them difficult to converge in simulations. In addition, traditional TCAD software lacks the capability to deduce device structural parameters from device performance metrics. This article selects four critical structural parameters of 4H-SiC trench gate MOS devices: trench depth (D_t), gate oxide thickness (T_ox), drift region doping concentration (N_d), and P-region channel P-region length (L) as variables. Firstly, two types of neural network architectures were constructed and trained to serve as a classifier and a value predictor, respectively, among them, the breakdown mechanism classifier achieved an accuracy rate of 97% in the validation process. The average error of breakdown voltage prediction was 5.6%. In order to ensure the accuracy and stability of the prediction, we randomly selected 1000 sets of parameters within the value range for simulation to obtain a new dataset and improve the neural network structure. The improved neural network achieved average errors of 2.9% and 4.9% in the prediction of breakdown voltage and on-resistance, respectively. Subsequently, we built an optimizer based on the improved neural network, achieving an automated design process for device structural parameters according to target breakdown voltage and on-resistance. In the accuracy validation of the optimizer, the average error between target values and actual values of breakdown voltage and on-resistance is 2.5% and 7.9%, respectively.

Metaheuristic algorithms play a crucial role in tackling the increasing complexity and challenges in antenna design. The nutcracker optimization algorithm (NOA), a novel metaheuristic inspired by nutcrackers' food-gathering, storing, searching, and retrieving behaviors, has shown excellent performance on 23 standard test functions and CEC—2014/2017/2020 test suites compared to well-established algorithms, yet it remains unapplied in antenna design. This study proposes a multi-strategy improved NOA (MINOA) to resolve NOA's unbalanced exploration and exploitation issues, applying it to ultra-wideband antenna design optimization and linear antenna array sidelobe suppression. MINOA employs Bernoulli chaotic mapping for uniform population initialization, a dynamic boundary strategy for balanced exploration and exploitation, and adaptive t-distribution disturbance to accelerate convergence and enhance local exploitation. Extensive tests on 23 benchmark functions prove MINOA's superiority in optimization accuracy, convergence speed, and stability over advanced algorithms such as NOA, WOA, GWO, SSA, DEA, SCSO, and HBMO. The Wilcoxon signed-rank test validates its significant improvement in accuracy. In broadband antenna optimization via an artificial neural network (ANN)-based surrogate model, MINOA reduces the mean square error (MSE) by 40.9% at the same iteration number and by 28.6% with 10 fewer iterations and 29 fewer fitness function calls compared to NOA during the preliminary training phase, achieving the widest bandwidth (3.62–11 GHz) among the eight algorithms. The Wilcoxon signed-rank test confirms MINOA's superiority. In the 16-element linear antenna array optimization, although MINOA performs slightly worse than DEA and WOA, it still achieves a low-sidelobe level of −41.38 dB, verifying its feasibility.

Higher-order boundary value problems (BVP) of differential equations (DEs) are important in the mathematical description of many real-world processes. Solving such problems for exact or analytical solutions is not always easy to deal. Therefore, to compute their numerical solution, we need some numerical methods. Hence, in this work, a powerful numerical procedure based on Haar Wavelet (HW) method is established to deal with fourteenth-order BVPs linear and nonlinear. A generalized form of the algorithm is developed under general boundary conditions. Then the numerical method is verified on various examples from the literature. Also, maximum and root mean square errors are calculated. Moreover, a comparison between exact and numerical results is shown at different collocation points. Furthermore, convergence rate is approximately 2 at various numbers of nodal points is also calculated.

The work presents the design of a compact two and four-element multiple input multiple output (MIMO) antenna, featuring a self-isolation capability. The proposed MIMO antenna operates at 0.77–0.96 GHz Global System for Mobile Communication (GSM-900) and 3.4–3.8 GHz (sub-6 GHz 5G band) for |S₁₁| < −10 dB. The front view of the designed antenna comprises a thin slot that is sandwiched between the patch and an additional radiating element. Further, an inverted T-shape ground has been incorporated to attain the dual-band functionality without increasing the size of the antenna. Four antenna elements are placed orthogonally to enhance isolation between the inter-spaced radiators. It provides high isolation of the order of 32 and 24 dB for the GSM and 5G bands, without the use of a decoupling element. The two-port and four-port MIMO antenna occupies a space of 38 × 23 mm² and 50 × 45 mm² on FR4 substrate with permittivity of 4.40. Furthermore, diversity characteristics have been evaluated based on crucial parameters like envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL) which are computed, and corresponding values for these parameters are as ECC 0.02, DG 10, and CCL below 0.5 bits/s/Hz for the two-port antenna. However, in the case of the four-port MIMO antenna, diversity results are as ECC 0.005, DG 10, and CCL is below 0.4 bits/s/Hz, which shows that the diversity performance of the four-port MIMO is better than the two-port MIMO antenna. Further, the validation of results and performance parameters of the fabricated antenna are experimentally tested and verified with the simulation results. The proposed work is well suited for Internet-of-Things (IoT) and Biomedical applications with SAR values at 0.80 GHz/3.60 GHz for two/four ports corresponding to 0.000153 and 0.712 W/Kg for single-port and 0.000473 and 0.0483 W/Kg for the four-port MIMO antenna.

This paper presents compact, low-power, electronically tunable dual-output filters implemented using DTMOS (dynamic threshold MOSFET) in a 0.18 μm CMOS process. The designs utilize intrinsic transistor parasitic capacitances to realize filter responses, eliminating the need for external passive components. Electronic tunability in both low-pass and band-pass outputs is achieved through the dynamic threshold MOSFET technique, allowing operation at a lower supply voltage with fewer components. The low-pass filter's cut-off frequency can be tuned from 237.2 kHz to 141.34 MHz, while the band-pass filter's center frequency ranges from 218.3 kHz to 132.13 MHz. Three filter configurations are proposed to optimize frequency performance. Simulation results using Cadence–Virtuoso show an average power consumption of 0.213 mW at 1.6 V with a 15 pF load capacitor. The proposed filter design is ideal for integration into modern portable and power-sensitive electronic systems due to its wide tuning range, low voltage operation, low power consumption, and compact size.

Aggressively scaled devices with high power density wrapped around the low thermal conductivity material in narrow space are susceptible to severe self-heating effect (SHE), especially gate-all-around FETs (GAAFETs). Conventional BSIM-CMG compact model—relying on first-order thermal RC networks—severely underestimates high-frequency transient SHE impacts. In this paper, a transient electrothermal co-simulation framework coupled BSIM-CMG with a high-order thermal RC network is developed to accurately capture self-heated temperature prediction. Self-heating temperature rise is calculated through a high-order thermal network in which each RC component represents a discrete thermal dissipation path corresponding to one specific thermal region. The high-order thermal network substitution is accomplished via an HDL module that redefines power generation and dissipation computations at each thermal node, followed by the integration into the BSIM-CMG model for enabling comprehensive and precise transient thermal evaluation in device-circuit level. In contrast to the conventional methods, the proposed framework can more effectively replicate the phenomenon of multiple thermal time constants present in transient temperature responses of GAAFETs. Its simulation results exhibit better consistency with the TCAD, with an error of less than 2% at a given frequency of 200 MHz. Moreover, the proposed framework is further validated by the electrothermal co-simulation of ring oscillators and differential amplifier.

Radio Frequency Integrated Circuits (RFICs) are pivotal in modern wireless communication systems, demanding advanced design methodologies to meet escalating performance requirements. This paper introduces a novel framework for the automated design of irregular hairpin filters, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. We propose two irregular design methodologies: random displacement and spline-based design, to enhance filter performance while ensuring manufacturability. The random displacement method incorporates obtuse angle detection to address fabrication challenges, while the spline-based approach achieves smoother transitions and faster convergence. Compared to conventional rule-based designs, both methods demonstrate significant improvements in return loss, insertion loss, and bandwidth. The random displacement method achieves a relative bandwidth of 21.74%, and the spline-based design achieves 20.25%, outperforming traditional approaches. These results underscore the potential of irregular design methodologies in advancing RFIC performance and manufacturability.

In 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.