Journal of Computational Electronics最新文献

Major advances in molecular diagnostics have fueled the search for nanosensors that can detect anomalies in their early stages of development. In this research work, we have investigated a tetracene molecule bridged between gold electrodes in a device configured for sensor application in medical diagnostics. Density functional theory (DFT) and non-equilibrium Green’s (NEGF) functions have been utilized to study the feasibility of tetracene molecular junctions for detecting the presence of arsenic and tracing its concentration. In this context, transmission spectra, molecular-projected self-consistent Hamiltonian (MPSH), current–voltage curve, conductance trends, and HOMO–LUMO gap (HLG) at different operating voltages are determined. Notably, during exposure of the molecular junction to varying concentrations of arsenic, substantial changes are detected in the electron transport properties. Both the conductance and current of the molecular junction escalates with the increase in impurity of the arsenic atoms, thus proving that tetracene is a suitable candidate to be explored as a nanosensor.

The density functional theory (DFT) with Becke’s three-parameter Lee–Yang–Parr (B3LYP) hybrid functional and the 6-311G(d,p) basis set was utilized to investigate the structural and conformational stability, the energies of the highest occupied molecular orbital (HOMO), and lowest unoccupied molecular orbital (LUMO), as well as related reactivity parameters, electrostatic potential, electronic transitions, nonlinear optical (NLO) properties, and dye-sensitized solar cell (DSSC) characteristics of six isomers of (E)-3-(i,j-dichlorophenyl)-1-(4′-nitrophenyl)prop-2-en-1-one, where i, j = 2–6 and i ≠ j. The s-cis conformers are more stable than the s-trans conformers. The syn conformers are more stable for five of the isomers than their anti-syn counterparts. The (3,5) isomer was the most stable among the isomers. All isomers demonstrated the ability to inject and recover electrons. The (2,5) isomer exhibited the highest exciton binding energy, while the (3,4) isomer showed the lowest dye regeneration driving force. The highest open-circuit voltage was observed for the (2,6) and (2,3) isomers. The (3,4) isomer had the highest light-harvesting efficiency, whereas the (2,5) isomer had the lowest. All chalcones exhibit higher first-order hyperpolarizability β values than urea, with the anti-(3,4) isomer having the highest β and the smallest (HOMO–LUMO) energy gap. TD-DFT (B3LYP/6-311G(d,p)) in the gas phase reveals that the chalcones display two UV bands. Band 1 arises from the electronic transition between the HOMO and LUMO. However, band 2 consists of electron excitation from HOMO and HOMO-2 to LUMO + 1. The chalcones investigated show promise as candidates for NLO and DSSC applications.

Graphical abstract

We present a compact and efficient three-port graphene-based circulator operating in the terahertz (THz) frequency range. Conventional designs are predicated on dipole resonances. In contrast, the present approach exploits the quadrupole mode of a circular graphene resonator, magnetized by a perpendicular direct current magnetic field. The structure is composed of a single-layer graphene resonator that is coupled to three graphene waveguides. These waveguides are supported by silica and silicon substrates. Through the optimization of resonator geometry and the tuning of graphene chemical potential, a substantial reduction in operational requirements was achieved, enabling functionality with a magnetic field of 0.2 T and a Fermi energy of 0.1 eV. Full-wave simulations performed in COMSOL Multiphysics demonstrate excellent nonreciprocal performance, with isolation better than –21 dB, insertion loss around –2.6 dB, and reflection of –18 dB at 5.38 THz. The frequency response is in good agreement with the predictions of temporal coupled-mode theory (TCMT), which confirms a fractional bandwidth of approximately 6.3% around the central frequency of 5.58 THz under the applied magnetic bias. A comparison of the proposed circulator with existing designs reveals a substantial reduction in both its physical dimensions and its weight. Furthermore, the circulator functions under conditions that demand less voltage and magnetic field strength than existing designs. In conclusion, the practical feasibility of device fabrication is discussed, with a focus on the compatibility of the proposed structure with current graphene-based photonic manufacturing technologies.

This letter presents novel approximate analytical solutions for modeling solar cells’ current–voltage (I–V) characteristics by applying resistance–diode (RD) circuit approaches. Three different approximation methods are developed and used to modified solar cell equivalent circuits, including single, double, and triple-diode configurations. The proposed solutions demonstrate excellent agreement with numerical simulations and experimental measurements, while achieving significant reductions in computational time. These features make the methods suitable for real-time applications in power electronics and smart grid environments. The approach provides a valuable analytical tool for enhancing photovoltaic modeling and strengthens the connection between circuit theory and solar energy systems.

We explore the application of artificial neural networks (ANNs) for predicting the millimetre-wave (mm-wave) and sub-millimetre-wave (sub-mm-wave) characteristics of double-drift region (DDR) Si IMPATT diodes. The proposed ANN models predict key parameters such as DC, large-signal (L-S) performance, and avalanche noise characteristics across frequencies ranging from 94 to 500 GHz. A dataset derived from self-consistent quantum drift–diffusion (SCQDD) simulations is used to train the ANN models, which accurately capture the influence of structural, doping, and biasing variations. The ANN models showed a significant reduction in computational time, predicting device characteristics in just 4.4–15% of the time required by SCQDD simulations, while maintaining high accuracy. The mean square error (MSE) between ANN predictions and SCQDD simulations for breakdown voltage and power output was observed to be in the order of 10⁻³ Unit², indicating excellent predictive performance. The models were validated against experimental data, showing strong agreement in terms of power output, efficiency, and noise characteristics. This work demonstrates that machine learning can effectively replace traditional time-intensive simulations, making it a promising approach for the rapid design and optimization of high-frequency semiconductor devices.

AlGaN-based deep ultraviolet laser diodes (LDs) have attracted considerable interest because of their diverse applications over the last two decades. The optimization of DUV LDs is essential to advancing high-efficiency photonic devices. However, traditional simulation tools are computationally intensive and slow, presenting challenges for iterative development of optoelectronic devices. We propose an AI-driven approach that leverages machine learning (ML) and explainable AI (XAI) to accelerate the design process of DUV LD and enhance understanding of the correlation between key LD performance parameters. Our methodology involves training ML models on a dataset of DUV LD design parameters to evaluate each model’s predictive accuracy. We also integrate XAI to assess input feature importance such as material composition and thickness of epilayers. This framework provides predictions for laser output power ((P_{text {out}})), laser threshold current ((I_{text {th}})), and optical confinement factor ((Gamma)) with R² values of 73%, 71%, and 80%, respectively, with the best-performing model that is extreme gradient boosting. This model substantially reduces the computational time required for optimum design iteration. These results demonstrate that our AI-based approach outperforms traditional methods in speed and resource efficiency, providing actionable insights into design parameters that align with physical mechanisms. This work establishes a resource-efficient AI framework that accelerates the development cycle of high-performance LDs.

This paper presents a numerical framework in MATLAB for solving the generalized nonlinear Schrödinger equation (GNLSE) using adaptive algorithms and the split Fourier method. It simulates soliton-wave interactions in optical fibers, taking into account high-order dispersion (HOD), nonlinear mechanisms (such as SPM, Raman, and Brillion), and the effect of soliton initial divergence. The results show that the dispersion coefficients (β₂ and β₄) govern the stability and interactions of solitons, causing phenomena such as spectrum splitting and the formation of dispersive waves. Mechanisms for controlling soliton fusion/repulsion via initial separation and relative phase are also revealed, with typical accuracy < 0.1%. The framework offers a computational speedup of up to 10 times, supporting the design of optical communication systems, frequency combs, and pulse compressors. The model can be generalized to study quantum phase transitions and soliton interactions in multilayer photonic crystals, with potential extension for future algebraic modeling.

Capacitive pressure sensors (CPS) have several applications and are widely used for pressure measurement. However, they have a significant disadvantage in terms of sensitivity versus dynamic range trade-off. Mostly, it is crucial and challenging to use CPS for noninvasive assessment of low pressure in medical flexible tubings. Diaphragm displacement of flat plate sensors due to a radial displacement of a fluid catheter is small. This makes the sensor insensitive because the transmission mechanism might not amplify the input displacement for minute but significant loads. Additionally, the dynamic range and sensitivity are reduced because of the small contact surface area between the catheter and a flat plate diaphragm. To address these challenges, we design and analyze a novel type of sensor, namely, the cylindrical shell capacitive pressure sensor (CS-CPS). CS-CPS allows increased contact surface area between the sensor and flexible tubings, thus enhancing input displacement, sensitivity, and simplicity of integration with flexible tubings. The sensor is designed and simulated in COMSOL Multiphysics. The finite element analysis method is utilized to analyze the diaphragm deformation and capacitance variations in response to pressure. For verification purposes, we do a mathematical analysis in MATLAB using the derived deformation and capacitance variation formulae. Compared to the flat plate sensor, the newly designed sensor achieved an increased diaphragm displacement of 2.49x10(^{-7} text{mm}) and sensitivity of 2.312x10(^{-21} text{pF/Pa}) without compromising the dynamic range. The CS-CPS has shown to be more effective than the flat plate sensor for noninvasive sensing of pressure in flexible tubings.

We propose a new radiation-hardened AlGaN/GaN High Electron Mobility Transistor (N-HEMT) structure with an AlGaN insertion layer integrated into a conventional gate field-plate configuration. The AlGaN insertion layer acts as a back-barrier, effectively minimizing leakage current in the buffer layer, which in turn boosts the breakdown voltage of the device. Due to the band discontinuity and bandgap difference between AlGaN and GaN, this layer creates a quantum well at the heterojunction interface, trapping radiation-induced electrons and blocking their entry into the conductive channel. This mechanism lowers the production rate of electron–hole pairs triggered by impact ionization, significantly enhancing the device’s tolerance to single-event burnout (SEB). By optimizing parameters using Sentaurus TCAD, the reinforced structure (N-HEMT) attains a breakdown voltage of 912 V and an SEB threshold of 600 V, reflecting gains of 77 V and 350 V, respectively, compared to the conventional standard AlGaN/GaN HEMT structure (T-HEMT). While slight reductions in output and transfer performance are observed, these are considered insignificant for practical use.

This in-depth exploration dives into the fascinating world of CslnTiS₄ quantum dots (QDs), uncovering their exciting potential for optoelectronics and photonics. Using X-ray diffraction (XRD), researchers found that these QDs have a unique monoclinic crystal structure, classified under (P21) space group. This sets them apart from typical perovskites and titanates, giving them a structural identity all their own. One standout feature is their direct forbidden bandgap of 2.22 eV, which is much smaller than the bandgaps of traditional titanate ceramics like CsAlTiO₄ (often above 3 eV). This narrower bandgap opens up new possibilities for visible-light applications. Advanced computational tools, like density functional theory (DFT), reveal strong interactions between the Ti-d % S-p orbitals, enhancing the material’s ability to absorb visible light. The authors highlight impressive optical properties, including high dielectric constants, refractive indices, and absorption coefficients, all pointing to excellent light–matter interactions. Notably, the material shows strong third-order nonlinear optical responses, making it ideal for cutting-edge photonic technologies. Swapping ln & S for Al & O in the CsAlTiO₄ framework adds even more flexibility, improving electronic transitions and boosting charge mobility. With such finely tuned bandgap, enhanced dielectric properties, and remarkable nonlinear behavior, CslnTiS₄–QDs emerge as a game-changing alternative to traditional titanates. These tiny but mighty quantum dots hold immense promise for applications in solar cells, photodetectors, and advanced nonlinear optical devices that usher in a new era of materials science innovation.