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

Numerical computation of capacitance matrices using conventional finite-difference (FD) technique for arbitrarily shaped multi-conductor problems which typically involve thin dielectric coating is challenging due to the fact that an extremely fine discretization of the computational domain is required to capture the nuances of the geometries involved, which, in turn, exacts a high computational resource cost—both in terms of memory and time. In this paper, we present a novel finite-difference-based technique which utilizes polynomial interpolation and extrapolation techniques in conjunction with the conventional finite-difference technique to handle 2-dimensional problems involving multiple conductors, typically with a thin dielectric coating. The proposed technique does not require fine discretization of the computational domain and provides accurate results in an efficient manner.

This study presents a novel approach for optimizing the parameters of monolithic microwave integrated circuit (MMIC) functional units using machine-learning techniques and multi-objective optimization algorithms. We utilize advanced machine-learning methods, including random forest, artificial neural networks (ANNs), and recurrent neural networks (RNNs), to construct highly accurate models that predict the performance of these units. These models are subsequently integrated with a multi-objective optimization algorithm, specifically the multi-objective particle swarm optimization (MOPSO), to generate inverse design solutions for both the geometric designs of the units and the fabrication parameters of the heterogeneous integration process. Our approach, which has been validated through chip fabrication and testing, has demonstrated its robustness as a tool for achieving optimal MMIC designs. It not only reduces the design time but also enhances the manufacturability of MMICs, thereby opening new avenues in microwave and RF circuit design.

This research article examines a L-shaped dielectrically modulated label free TFET (L-DM-TFET) biosensor for the purpose of detecting different biomolecules using a label-free biosensing detection technique. The proposed structure allows for the recognition of biomolecules by modulating various electrical properties, such as the drain current, transconductance, and linearity parameters. The source region of the proposed TFET incorporates a SiGe (source)/Si (channel) heterojunction, utilizing a low bandgap material of SiGe. This heterojunction is employed to enhance the ON-state current of the devices. The materials used and the fabrication steps involved in our proposed device are compatible with complementary metal-oxide-semiconductor (CMOS) technology. This analysis is conducted using a calibrated Silvaco technology computer-aided design (TCAD) simulator. Additionally, by considering a dielectric constant range of 1–12, we calculate various figure of merits (FOMs) parameters for the device. These include evaluation of linearity, sensitivity, and noise characteristics. Furthermore, we have conducted an analysis of linearity FOMs, such as VIP2, VIP3, IIP3, and IMD3 for the proposed device under study. Additionally, the linearity analysis of the presented tunneling FET (TFET) indicates the device's excellent performance in distortionless switching operations. Consequently, the L-shaped dielectrically modulated biosensor holds potential suitability for high-speed circuit designs.

This study presents the design and implementation of electromagnetic energy harvesters for the purpose of charging unmanned aerial vehicles (UAVs) battery. In the study, the designed harvesters are analyzed through finite element method (FEM) simulations. In the FEM analysis, common and self-inductance values, as well as magnetic flux density values of the harvesters, are calculated at specific current values. Inductance values are also theoretically calculated for comparison. Subsequently, an experimental setup is established to test the designed harvesters. After winding the core, the induced voltage and the power transferred to the load by the harvesters are measured. Curve fitting is performed after the measurements with different load resistances to find the maximum power transferred to the load. Through curve fitting, the maximum power obtained at each current value and at which load resistance this power is harvested are determined. Considering the intention of using the designed cores to charge UAVs and the importance of weight in UAV flight, the weights of each core, both without winding and after winding, are measured, and their costs are calculated. Taking all these criteria into account, the performance of the harvesters is demonstrated, and those among the used cores that are the most suitable for UAVs are identified in the study.

Threshold voltage behavior at cryogenic temperatures is dominated by interface traps. This mechanism leads to different trends of the threshold voltage for NMOS and PMOS toward deep cryogenic temperature. This study investigates threshold voltage (V_th) at cryogenic temperatures down to 10 mK for the first time, based on the recently developed physical charge-based analytical threshold voltage model. To investigate the impact of devices on circuits at low temperatures, crucial MOSFET and analog design parameters, including transconductance (g_m), subthreshold swing (SS), linear region current (I_lin) and g_m/I_DS related parameters are characterized and compared from 300 to 4 K. A Discussion on circuit performance and power consumption has been conducted to provide useful insights for low-temperature CMOS circuit design.

In this paper, a new frequency domain behavior model for radio frequency (RF) power transistors based on a hyper-parameter optimized Gaussian process regression (GPR) method is presented. The chaos game optimization (CGO) algorithm is used to optimize GPR hyperparameters, resulting in the CGO-GPR model. The basic theory as well as the details of the modeling process are presented. Validation of the model is conducted using a 10-watt GaN power transistor. Compared to the standard GPR model, the proposed model achieved a significant improvement. Furthermore, the comparison with the particle swarm optimization (PSO) based GPR model (PSO-GPR) showed that the proposed model allows achieving superior performance, thereby confirming the effectiveness of the developed modeling technique.

The current manuscript investigates a six compartmental mathematical model for malicious Codes in Wireless Sensor Network is consider for investigation under the fractional operator of Caputo along with their numerical scheme. The six agent nodes of the network sensors are transferable like in infection with in their community of different nodes. With the help of fixed point theory the presentation of existence and uniqueness of solution of the said model are also given. The scheme of numerical solution under fractional format is developed with the choice of fractional orders which increasing the degree of freedom for such type of network analysis. The numerical simulation of all the six agents are given on different fractional orders along with sensitivity of the fractional orders and some used parameters. The new analysis artificial neural network (ANN) method has been utilized for the considered model and compared with Adams–Bashforth (AB) method. We divided the data set into three categories training, testing and validation with ANN method and the analysis is presented in this work.

Selecting designs that efficiently optimize multiple objectives simultaneously is an important problem in several distinct industries. Typically, there is not a single ideal design; rather, there are several Pareto-optimal designs that provide the best possible trade-offs between the objectives. However, evaluating every design might be expensive, making a thorough search for the whole Pareto optimum set impractical. The aforementioned issue with technology computer-aided design (TCAD) while investigating a multidimensional parameter set for device design is addressed using Pareto active learning (PAL) and the nondominated sorting genetic algorithm-III (NSGA-III) which are metaheuristics-based multiobjective optimization (MOO) techniques. NSGA-III adeptly analyzes the tradeoffs among multiple objectives while ensuring diversity in the design space. PAL forecasts the Pareto-optimal set with intelligence by deliberately sampling the design space. This work focusses on improving the performance of surrounding gate tunnel field-effect transistors (SGTFETs) by optimizing and assessing their complex designs in terms of multiple objectives, including power, energy, speed, and variability. This paper presents a novel MOO framework that incorporates machine learning (ML) approaches, including NSGA-III and PAL in SGTFETs technology. The framework provides effective global optimization without gradients, allowing for the automatic recognition of the best solutions. The outcomes show the possibility of ML-based MOO to create next-generation nanoscale transistors.

This paper proposes contributions to the efficient wave digital (WD) modeling of large oscillator networks which are emerging as energy-efficient alternatives to traditional computers. The WD concept enables in-operando parameter tuning, real-time testing, and the associated algorithms are highly parallelizable. We present a general electrical model of N-shaped nonlinearities that are commonly found in nonlinear oscillators. Our model offers the flexibility to design the current–voltage characteristic based on specific requirements. We show how this model can be used to derive efficient and explicit WD algorithms for nonlinear oscillators. Furthermore, we propose the use of lossless transmission lines between the oscillators and the coupling network to obtain an ideal circuit for an oscillator network that can function as an Ising machine and be efficiently and exactly evaluated in the WD domain. The proposed algorithms are compared against the classical method involving iterative techniques, and their capabilities are evaluated through the emulation of a single FitzHugh-Nagumo oscillator as well as an Ising machine involving transmission lines. In the latter case, we show that, for large networks, the proposed methods decrease the runtime by up to 75% compared to using iterative techniques.

This article presents three different higher-order inverse filter (IF) configurations using current differencing buffered amplifier (CDBA) as an active component and a few passive elements. The topology can be used to synthesize fourth-order inverse low pass filter (FO-ILPF), inverse band pass filter (FO-IBPF), and inverse all-pass filter (FO-IAPF) using suitable admittance combinations. PSPICE simulations verify the functionality of the proposed IFs with the CMOS-based CDBA using 180 nm technology. The theoretical analysis and simulated results are also carried out, which shows that they are in close agreement. The passive sensitivity analysis, non-ideality analysis, Monte Carlo simulation, temperature analysis, percentage of total harmonic distortion (%THD), and noise analysis of the proposed filters are also performed. The proposed design has also been implemented using the current feedback operational amplifier (CFOA) as IC AD844AN to verify the functionality.