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,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 V_B,eq≈ 1.05 kV , f_T≈ 118 GHz , f_max≈ 326 GHz , NF_min≈ 1.48 dB , and T_max≈ 373 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 Iinit, enabling principled initialization. Second, it adapts concise and explicit iterative expressions that map Iinit directly to the estimated current Ical, establishing a clear functional mapping Ical=F(Iinit) 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
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
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 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.
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 × 10 ^5 and a high sensitivity of 800 nm/RIU. It also attains a high figure of merit of 4.151 × 10 ^5 RIU ^-1 and a detection limit of 2.409 × 10 ^-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 μm^2 makes the device well-suited for integration into photonic chips and diverse sensing platforms.
