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

Characteristic mode (CM) analysis serves as a powerful tool for evaluating the radiation and scattering characteristics of objects. CM formulations within the method of moments (MoM) framework are widely favored due to their ability to offer clear physical insights, handle complex shapes, and facilitate straightforward implementation. However, MoM-based CM formulations become inefficient when applied to electrically large objects due to the dense matrices involved. This article introduces a novel approach using a fast low-rank decomposition-based implicitly restarted Arnoldi method (IRAM) to accelerate CM computations. The adaptive cross approximation (ACA) and QR-SVD algorithms are employed to efficiently compute the low-rank decomposition of matrices. The ACA-QR-SVD algorithm offers advantages in matrix filling, LU factorization, and matrix–vector multiplication processes, thereby enhancing efficiency. Numerical simulations on two representative objects demonstrate that the proposed algorithm notably improves computational speed and reduces memory requirements while maintaining high computational accuracy.

This paper developed the novel structure of a dual material double gate negative capacitance tunnel field effect transistor (DMDG-NC-TFET) using HZO ferroelectric material. This study systematically improved the drain current and subthreshold swing (SS) by inducing a negative capacitance effect in a gate stack. The proposed gate oxide structure is a stack configuration of ferroelectric material, and high-k dielectric to improve gate control. The Landau–Khalatnikov (LK) equation is used to solve the Poisson equation and get an accurate estimate of the channel potential. Kane's model is used for band-to-band generation rate calculation. For modelling the drain current, the band-to-band tunnelling (G_btbt) generation rate is integrated using the entire device volume. The impact of varying ferroelectric thickness in the proposed structure has been investigated with the simulated results. The outcomes demonstrate that the device can obtain better improvements in ON current and SS, compared to conventional DMDG-TFET. By contrasting the analytical results with the outcomes of the TCAD simulation, the effectiveness of the proposed methodology has been demonstrated.

Deep learning has achieved better results in natural language processing, computer vision, and other fields. Nowadays, more deep learning algorithms have also been applied in brain-based emotion recognition. In the studies on brain-based emotion recognition, deep learning models typically use one-dimensional time series as the input and cannot fully leverage the advantages of the models in image classification or recognition. To address this issue, based on the publicly available SEED and DEAP datasets, the Gramian angular difference field (GADF) method was proposed to construct two-dimensional image representation datasets: SEED-GADF and DEAP-GADF datasets, in the paper. Additionally, a convolutional attention mechanism model (AMB-CNN) was introduced and its classification performance was validated on SEED-GADF and DEAP-GADF datasets. AMB-CNN achieved an average accuracy of 90.8%, a recall rate of 90%, and AUC of 96.86% on SEED-GADF. On DEAP-GADF, the average accuracy, recall rate, and AUC respectively reached 96.06%, 96.06%, and 98.58% in the valence dimension and 96.11%, 96.11%, and 98.73% in the arousal dimension. Finally, the comparison results with various algorithms and ablation experiments proved the superiority of the proposed model.

This paper introduces a new design method for a high-power density GaN MMIC amplifier operating in the Ku-band. A thermal model to investigate the thermal distribution of power amplifiers is proposed to achieve optimal performance in terms of power density, chip size, and channel temperature. The thermal distribution and channel temperature of a single device, an eight-way parallel device combination, and the entire PA layout are obtained by finite element simulation. The thermal coupling effects of high-power MMICs are analyzed in detail. The thermal resistances are extracted from the simulation to design a Ku-band amplifier. Measurement results demonstrate that the designed amplifier achieves 43.0–44.2 dBm output power and 22.7%–34.5% PAE at 28 V drain voltage with a 100 μs pulse width and 10% duty cycle within 12–18 GHz. The proposed design method enables the amplifier to have a compact layout of 10.88 mm² and a power density between 1.84 and 2.42 W/mm. This design method can offer valuable insights for future development of high-power MMIC amplifiers.

This article introduces a one-step leapfrog three-dimensional (3D) split-field finite-difference time-domain (SF-FDTD) method designed for analyzing periodic structures under oblique incidence, aiming to improve computational efficiency. Initially, it introduces new variables to substitute the field components postsplitting. After that, it applies time-centered approximate difference method to precisely adjust the time step for each iteration. It eliminates the need for empirical coefficients when performing calculations with lossy materials. Finally, it derives the implementation of the convolutional perfectly matched layer (CPML) for the proposed method. The proposed method is both easier to implement and more resource-efficient, significantly cutting down CPU usage and memory consumption. Numerical results confirm its improved efficiency.

The error tolerance nature of the digital multimedia applications enables the implementation of approximate digital circuits to achieve the benefits of high speed of operation and low power consumption. This paper proposes a static approximate modified mirror full adder (SAMM-FA) circuit designed using logic level approximation to reduce the number of transistors in the circuit. Owing to the balanced electrical characteristics, better stability and higher on-current to off-current ratio (I_on/I_off), 32 nm carbon nanotube field effect transistor (CNTFET) technology has been used for implementing the proposed circuit in the Cadence Virtuoso tool. Featuring only 10 transistors and operating at a supply voltage of 0.5 V, the proposed SAMM-FA has a low power dissipation of just 4.14 nW, and propagation delay of just 3.82 ps. The power delay product and energy delay product figure of merits of the proposed circuit are found to be excellent when compared with the contemporary designs.

This article studies the commutativity and sensitivity of the Lamé linear time-varying systems (LTVSs), and investigates the effects of disturbances on such systems. The commutative pair for the Lamé LTVS $��A�$ of order $��2�$ is found, that is, a new Lamé LTVS $��B�$ of order $��m�\le �2�$ is derived using the explicit commutative theories for zero initial conditions (ICs). For the case of nonzero ICs, the commutativity between the connected input–output of Lamé systems $��\mathrm{AB}�$ and $��\mathrm{BA}�$ is studied. New and simple explicit commutative theories and conditions for second-order LTVSs are derived, simplifying the use of commutativity for practical and industrial scenarios. These findings enable us to analyse the commutativity, sensitivity, robustness and stability of Lamé systems, and to determine the effects of disturbances. The explicit results presented in this article are supported by simulations and verified by examples and constitute a significant contribution to science and engineering applications.

In this paper, we present a numerical scheme based on a modified form of shifted Chebyshev polynomials to find the numerical solution of a class of fractional differential equations. For this purpose, we work out operational matrices of fractional integration of $��\psi �$-shifted Chebyshev polynomials obtained from shifted Chebyshev polynomials. Finally, the solution to the problem under consideration is obtained by solving a system of algebraic equations that results from the use of operational matrices of integration. The analysis of integer and non-integer order differential equations is presented to show the convergence of the solution of fractional order differential equation to the corresponding solution of the integer order differential equation. At the end, we present some linear and non-linear examples to validate the theoretical analysis. Non-linear examples are solved using Quasilinearization and proposed numerical technique.

All the time delayed dynamical system plays a vital role in describing the dynamical phenomenon of neural networks. In the current article, we study a class of 5D delayed bidirectional associative memory (BAM) neural networks that conform to objective reality. First of all, we prove that the solution of the delayed 5D BAM neural networks exists and is unique by virtue of fixed point theorem and some inequality techniques. Secondly, the Hopf bifurcation and stability of the delayed 5D BAM neural networks are investigated by exploiting the stability criterion and bifurcation theory. Once more, Hopf bifurcation control strategy of the delayed 5D BAM neural networks is explored by virtue of two different hybrid controllers. By adjusting the parameters of the controllers, we can control the stability domain and Hopf bifurcation onset. Eventually, the correctness of the theoretical results was verified through numerical simulations. The conclusions obtained in this paper are new and have important theoretical value in neural network area.

In wireless sensor networks, conserving power is vital for prolonging battery life. This research introduces a groundbreaking solution: a 9T carbon nanotube-field effect transistor (CNTFET) based SRAM cell (9T SRAM) designed to optimize power consumption and stability. Through meticulous analysis, the performance of this 9T SRAM cell is quantified. Power consumption metrics reveal impressive figures: the write, hold, read, and dynamic power are measured at 0.21 nW, 0.32 nW, 15.28 μW, and 8.09 μW, respectively. Furthermore, the Write SNM (WSNM), Hold SNM (HSNM), and Read SNM (RSNM) are found to be 380.11, 390.22, and 390.31 mV, respectively, indicating robust stability. The proposed bit cell has a write and read delay of 95.1 and 39.6 pS, respectively. Incorporating stacked transistors diminishes power consumption, while the decoupled read technique boosts the stability of the proposed bit cell. By comparing these results with existing SRAM cells, the superiority of the proposed 9T SRAM cell in terms of power efficiency becomes evident. Notably, it outperforms earlier models, making it an ideal candidate for integration into wireless sensor networks. These findings are supported by simulations conducted using HSPICE, alongside a 32 nm CNTFET model sourced from Stanford University.