Journal of Computational Electronics最新文献

Enhancement-mode MgZnO/ZnO high-electron-mobility transistors are promising candidates for oxide-based RF and power-oriented electronics, but their design requires simultaneous control of electric-field crowding, RF parasitics, electrothermal loading, trap-induced dispersion, and fabrication variability. Present MgZnO/ZnO studies usually examine these aspects distinctly, whereas field-plate and robust-optimization notions established mainly for GaN-based HEMTs cannot be directly transferred without material-specific recalibration. This work presents a literature-calibrated, physics-constrained multi-fidelity computational context for the co-design of an asymmetric segmented-field-plate MgZnO/ZnO HEMT. The work combines reduced-order device relations, a two-dimensional finite-difference electrostatic benchmark, an electrothermal correction, trap-dispersive small-signal equivalent-circuit modeling, multi-objective robust selection, and local Sobol sensitivity analysis. All numerical values reported in this study are outputs of the stated theoretical-computational benchmark; (V_{{B,{text{eq}}}}) is a model-derived field-limited comparative index. Within the stated benchmark, the robust-knee design reduces the local gate-edge electrostatic metric by approximately 36% compared with the unplated baseline and gives (V_{{B,{text{eq}}}} approx 1.05,{text{kV}}), (f_{T} approx 118,{text{GHz}}), (f_{{{text{max}}}} approx 326,{text{GHz}}), (NF_{{{text{min}}}} approx 1.48,{text{dB}}), and (T_{{{text{max}}}} approx 373,{text{K}}). The dispersive small-signal model decreases the complex broadband S-parameter error relative to a constant-element model in the synthetic high-fidelity scale. The results indicate that segmented field shaping, dielectric coupling, copper-aware parasitic reduction, trap dispersion, and uncertainty-aware design must be treated together. The study delivers a reproducible computational route for future experimental validation by matched baseline, continuous-field-plate, and segmented-field-plate MgZnO/ZnO devices.

The four-diode model (FDM) enables high-accuracy photovoltaic characterization but poses numerical challenges in solving its implicit current–voltage relationship. This paper modifies the state-of-the-art Iterative Lambert W Function (ILWF) methodology to propose a direct and physically transparent iterative framework. First, it replaces the dimensionless iteration variable in ILWF with a physically meaningful initial current guess I_init, enabling principled initialization. Second, it adapts concise and explicit iterative expressions that map I_init directly to the estimated current I_cal, establishing a clear functional mapping I_cal=F(I_init) without implicit dependencies. Third, it designs two goal-oriented termination criteria: Criterion 1 for parameter extraction using root-mean-square error (RMSE) monitoring, which requires measured current data; and Criterion 2 for parameter extraction and current prediction using a fixed three-iteration maximum-absolute-difference selection strategy. Validated on five photovoltaic devices under diverse conditions and measurement noise, the proposed framework outperforms ILWF and six recent FDM solvers. For parameter extraction, it reduces RMSE by 3.07–41.3% and average computation time by up to 75.4%. Improvements are statistically significant via one-tailed Wilcoxon signed-rank tests across 128 independent configurations; 18 non-significant cases (14.1%) arise from deterministic initialization–solver interactions rather than stochastic variability. Under noise, win rates against ILWF reach 74.5–92.8%. For current prediction on xSi12922 and mSi0188 modules under varying operating conditions, the framework exhibits superior extrapolation capability, and this advantage persists after enhancing physical consistency via the proposed diode ideality factor correction. This work provides a rigorously validated, efficient, and interpretable paradigm for solving the FDM, advancing high-fidelity photovoltaic modeling.

Inverse electromagnetic (EM) design problems often exhibit non-unique mappings, where multiple geometrical configurations can produce identical electrical responses. Conventional artificial neural networks trained using standard loss functions typically learn an averaged solution, limiting their ability to accurately model such multivalued relationships. This paper proposes a Multivalued Neural Network (MVNN) framework for inverse modeling of ferrite-based tunable rectangular microstrip antennas (RMSAs). The proposed architecture employs a feedforward neural network with a multibranch output structure, enabling the simultaneous prediction of multiple candidate geometrical solutions for a given set of electrical specifications. A custom multivalued loss function is introduced to guide the training process by selecting the prediction branch with the minimum error for each sample, allowing the network to learn multiple feasible inverse mappings without penalising alternative solutions. The model is trained using datasets generated from EM simulations based on the spectral-domain immittance approach (SDIA). The influence of the number of output branches M is systematically investigated. Experimental results show that the optimal configuration (M = 6) achieves approximately 42% reduction in mean squared error compared to a conventional single-output ANN while maintaining high prediction accuracy with R² > 0.999. The proposed MVNN framework provides an effective and computationally efficient approach for solving multivalued inverse EM design problems.

Materials databases built from high-throughput simulations often contain rare property classes, so classifiers trained on them face severe class imbalance. We benchmarked imbalance-handling strategies on 9,036 compounds from one such database, the BoltzTraP (Boltzmann Transport Properties) ab initio transport database, of which 445 (4.92%) have a hole effective mass ({m}_{p}^{*}) < 1.0 ({m}_{e}) (imbalance ratio 19.3:1). The inputs were three n-type descriptors: the electron effective mass, the n-type power factor, and the n-type Seebeck coefficient. Because the inputs and the label come from the same calculation, the classifier is not a screening tool; the task serves as a controlled testbed in which every label is known. All resampling was confined to training folds, as standard practice requires. Relative to no resampling, the borderline synthetic minority oversampling technique (Borderline-SMOTE) with eXtreme Gradient Boosting (XGBoost) raised minority-class recall from 0.411 to 0.798 and gave the highest F2-score (0.608). It also exceeded random undersampling on F2-score, F1-score, and Matthews correlation coefficient (MCC) (0.608 vs. 0.550; 0.448 vs. 0.355; 0.461 vs. 0.391). The no-resampling baseline, however, had the highest F1-score (0.520) and MCC (0.523), so the preferred strategy depends on whether recall or precision matters more. With fivefold, the fold-level Wilcoxon tests could not reach significance. The n-type descriptors carried information about the hole-mass class, an expected result because both carrier masses derive from the same band structure.

Biosensors based on surface-plasmon resonance have attracted considerable attention for rapid, label-free, and highly sensitive biomedical detection. In the present study, a comparative investigation of five two-dimensional (2D) materials namely graphene, molybdenum disulfide (MoS₂), tungsten disulfide (WS₂), hexagonal boron nitride (h-BN), and black phosphorus (BP) is presented using a multilayer SPR configuration. Initially, the sensing performance of each 2D material was systematically evaluated over a sensing medium refractive index range of 1.330–1.335 RIU. The sensing characteristics of SPR sensor are analysed in terms of resonance angle, sensitivity, full width at half maximum (FWHM), detection accuracy (DA) and figure of merit (FOM). The results demonstrate that the sensing performance is strongly influenced by the optical properties of the incorporated 2D material with BP exhibiting the most favourable overall sensing characteristics. Based on this comparative analysis, the optimized BP integrated SPR sensor was further investigated for the detection of HST6 Gastric Cancer cells. The optimized SPR sensor achieved a maximum sensitivity of 419.27°/RIU, FWHM of 5.1360°, DA of 0.1947 deg⁻¹ and FOM of 81.6341 RIU⁻¹. This result demonstrates the potential of the proposed BP based SPR sensor as a promising platform for HST6 Cancer detection.

A compact two-dimensional photonic crystal (2D-PhC) nanocavity biosensor based on a silicon-rod triangular lattice is proposed for the detection of cancer cells, blood components, and glucose concentrations in urine. The novelty of the proposed design lies in the systematic optimization of the sensing-rod radius to achieve an effective balance between optical confinement, resonance quality, and refractive-index sensitivity, thereby enabling simultaneous high-Q and high-sensitivity operation in a compact photonic structure. The band structure is obtained using the plane-wave expansion (PWE) technique, while the sensor performance is evaluated using the finite-difference time-domain (FDTD) method. To evaluate the device behavior, transmission spectra and the corresponding resonance wavelength shifts are thoroughly examined. Analyte detection is based on monitoring refractive index variations of different analytes under excitation from a 1550 nm light source. Furthermore, the influence of the detection rod radius ((R_D)) is systematically explored to achieve optimal performance. The proposed sensor demonstrates superior characteristics, achieving a high quality factor of 8.6399(times)10(^{textrm{5}}) and a high sensitivity of 800 nm/RIU. It also attains a high figure of merit of 4.151(times)10(^{textrm{5}}) RIU(^{mathrm{-1}}) and a detection limit of 2.409(times)10(^{mathrm{-7}}) RIU, indicating its strong sensing capability. The impact of fabrication variations is further analyzed, showing a significant tolerance to imperfections of up to 20 nm, indicating high fabrication yield and practical feasibility. Finally, the compact footprint of 105 (mu text {m}^2) makes the device well-suited for integration into photonic chips and diverse sensing platforms.

In this DFT-based theoretical study, I investigate the conductance superposition in π-conjugated molecular junctions containing single- and double-backbone transport pathways terminated with thiol (–SH), amine (–NH₂), and direct carbon (–C) anchoring groups. The electrical conductance is evaluated using density functional theory combined with quantum transport calculations to systematically compare the transport properties of single- and double-backbone molecular junctions. The results demonstrate that introducing a second molecular backbone consistently enhances the electrical conductance through constructive quantum interference between parallel transport pathways. However, the magnitude of this enhancement depends strongly on both the molecular structure and the anchoring groups, leading to systematic deviations from classical conductance additivity. Quantitative comparison with the classical (({2G}_{1})) and ideal quantum (({4G}_{1})) reference models shows that the conductance of double-backbone junctions consistently exceeds the classical limit while remaining below or approaching the ideal quantum limit, depending on the molecular system and anchoring group. For thiol-anchored junctions, the predicted conductance trends are in good agreement with previously reported scanning tunneling microscopy break-junction measurements, supporting the predictive capability of the computational approach. Overall, this study establishes a general computational framework for evaluating conductance superposition in molecular junctions and demonstrates that coherent quantum interference, together with molecular structure and anchoring chemistry, governs charge transport beyond the predictions of classical circuit theory. These findings provide fundamental insight into charge transport in molecular-scale electronic systems and offer design guidelines for the development of future high-conductance molecular junctions based on multi-pathway architectures.

This work establishes a finite element drift–diffusion framework that integrates polarization sheet charge boundary conditions and Shockley–Read–Hall (SRH) recombination to accurately model an InGaN nanowire HEMT biosensor. The formation of the two-dimensional electron gas (2DEG) and two-dimensional hole gas (2DHG) is a result of both spontaneous and piezoelectric polarization at the subband discontinuities of the InGaN/GaN interface. Analysis reveals that the overall effect of the 2DEG and 2DHG significantly influences the filling of quantum levels, with maximum sheet carrier charge densities of approximately 1.5 × 10⁻¹³ cm⁻² occurring at a distance of 2 nm, followed by a secondary peak around 13.5 nm. The electrostatic potential assessments indicate a notable energy band bending of − 3.0 eV at 5 nm at the heterojunction interface. The sheet density is calculated using a combination of Schrödinger–Poisson equations and quantum-corrected drift–diffusion methods, which are then integrated over the confinement region. The biosensing mechanism is effectively incorporated as a surface charge density at the gate dielectric’s top boundary, resulting in a threshold voltage shift that is influenced by quantum, electrostatic effects and the charge density profile. The coupled system comprises Poisson’s equation along with continuity equations for both holes and electrons. This setup includes drift–diffusion currents and SRH recombination, while the interface jump conditions necessitate the inclusion of polarization charges. By meticulously parameterizing material properties such as subband offsets, effective sheet carrier densities, total polarization constants, mobilities and trap lifetimes, the simulation successfully reproduces I_d–V_g characteristics, 2DEG/2DHG distributions, electrostatic effects, charge density profiles, as well as wave functions and wave probability density functions corresponding to quantum energy levels relevant to breast cancer-related biomolecular interactions. This comprehensive modelling framework lays the foundation for advancing the design and effectiveness of biosensors aimed at detecting critical biomolecular signals.

Non-small cell lung cancer (NSCLC) makes up the bulk of lung malignancies and is most often identified only after the disease has advanced, when curative options are scarce and prognosis is poor. CYFRA 21–1, a soluble fragment of cytokeratin 19 shed into the bloodstream during epithelial tumor-cell turnover, is a well-established serum indicator of NSCLC and an attractive target for minimally invasive screening. In this work, we construct an in silico model of a quartz crystal microbalance (QCM) immunosensor for CYFRA 21–1 and use it to probe how the choice of binding kinetics and the density of immobilized antibody together shape the sensor response. Capture of the antigen was simulated at the following analyte concentrations (1, 5, 10, 25, 50, 75 and 100 ng/mL) and three fractions of the theoretical maximum antibody density (10%, 50% and 100%) under two kinetic descriptions, the Langmuir adsorption model and a first-order binding model, producing 84 runs in total in Python 3.9. The Langmuir formulation, which imposes a finite pool of binding sites and reversible association, reproduced surface saturation and returned frequency-shift predictions in line with the physical behavior of a QCM, whereas the first-order model, having no saturation constraint, consistently overstated the response. At 10% antibody coverage, the modeled device reached a limit of detection of 0.014 ng/mL over a linear window of 1–100 ng/mL (R² > 0.9999). Fractional site occupancy stayed under 2% throughout, keeping the sensor within the Henry region of the isotherm and guaranteeing a proportional, predictable signal even at trace analyte levels. Collectively, these findings supply a quantitative design basis for inexpensive, reusable QCM platforms intended for early NSCLC detection.