Journal of Computational Electronics最新文献

Two-dimensional (2D) van der Waals multiferroics constitute an innovative platform for exploring coupled electronic and magnetic phenomena at the atomic scale. Here, we investigate monolayer MnI₂, an antiferromagnetic (AFM) material with intrinsic spin–valley coupling and geometrically frustrated lattice symmetry, to elucidate its magnetoelectric responses under the application of external electric fields (E-fields). Using advanced first-principles calculations, we demonstrate that MnI₂ exhibits a semiconducting electronic structure with spin-polarized valleys governed by strong electron correlations and asymmetric d-p hybridization. A dynamic interplay between in-plane E-fields and the material’s triangular Mn sublattice governs a competition between ferromagnetic (FM) and antiferromagnetic (AFM) exchange interactions, resulting in oscillatory magnetoelectric coupling and anisotropic phase transitions. Directional selectivity emerges as a hallmark: in-plane fields induce valley selective metallicity and modulate magnetic anisotropy through ligand-mediated charge redistribution, whereas out-of-plane-oriented fields preserve interlayer magnetic coherence and valley degeneracy. This anisotropy is further amplified by spin–valley locking, where E-field-driven charge transfer creates a feedback loop between valley polarization and magnetic moment reorientation. The material’s ability to host electrically tunable AFM-FM transitions, coupled with its compatibility with van der Waals heterostructures, positions MnI₂ as a promising candidate for quantum hybrid heterostructures. Our findings establish a framework for engineering 2D multiferroics with coupled spin, charge, and valley degrees of freedom, paving the way for low-power spintronic and valleytronic nanodevices.

Hole transport materials (HTMs) with uniform films, appropriate band alignment, high hole mobility, and processability are crucial for effective perovskite solar cells (PSCs). Herein, we designed and investigated four triphenylamine-based HTMs (M1 to M4) using thiophene-bridged acceptor engineering. Our results revealed that M1–M4 HTMs possess more negative HOMO energies, high solubility, narrower bandgaps, and maximum absorption ranging from 395 to 463 nm, along with lower reorganization energies compared to the reference HTM (R). The engineered M1 to M4 HTMs exhibit lower binding energy values, particularly those with electron-withdrawing groups, indicating enhanced exciton dissociation and improved charge transfer. The TDM analysis further demonstrated that these HTMs exhibit higher exciton dissociation and reduced electron coupling. The open-circuit voltage of the studied HTMs is 2.21 eV (R), 2.51 eV (M1), 2.46 eV (M2), 2.49 eV (M3), and 2.44 eV (M4), highlighting their potential as promising materials for PSCs. The incorporation of thiophene-bridged end-capped acceptors proves to be an effective strategy for developing high-efficiency materials for PSCs. Thus, the engineered M1 to M4 HTMs demonstrate significant promise for application in the solar industry.

Graphic Abstract

In this paper, we focus our study on the magnetic photonic crystal fiber (MPCF) made of cerium-substituted yttrium iron garnet (Ce: YIG), which contains magnetic fluid (MF) in the air holes. Like several other optical devices, isolators utilize a phenomenon called Faraday rotation (FR) to prevent reflections. FR rotates linearly polarized light when it travels parallel to a magnetic field. Cerium-substituted yttrium iron garnet (Ce: YIG) exhibits low optical absorption at telecommunication frequencies and a large Faraday rotation coefficient. The variations in mode conversion from TE to TM as a function of the gyrotropy parameter (g) for TE and TM polarizations are numerically simulated at the telecommunication wavelength λ = 1.55 μm. We demonstrate FR and modal birefringence following polarization and gyrotropy. We observe an increase in FR and modal birefringence for TM and TE polarizations as g increases. We propose MPCF for integrated magneto-optical applications based on these findings. Moreover, a new isolator built into a photonic crystal fiber is constructed using Ce: YIG and MF. The results indicate that the two modes periodically exchange power. The impact of gyrotropy on the coupling length is evident. The results show that the two modes periodically exchange power. The influence of gyrotropy on the coupling length is evident. Additionally, the findings indicate that FR and modal birefringence directly affect TE-TM-mode conversion, with Faraday rotation (FR) reaching 8940°/cm and modal birefringence (ΔN) of 40.8881 × 10⁻⁴. This effect is also considerably stronger than in conventional fibers.

Perovskite solar cells have been under extensive investigation for the past few decades as they promise higher efficiency and lower cost of production compared to current silicon-based solar cells. The implementation of any perovskite either in single or multijunction solar cells is mainly dependent upon its energy bandgap. The existing methods to determine and predict the bandgap of a perovskite are time-consuming, expensive and resource intensive. In this work, we discuss leveraging machine learning algorithms and techniques to identify the key compositions influencing the bandgap of lead (Pb) and tin (Sn)-based perovskite solar cells. For Pb-based perovskite solar cells, Cs_aFA_bMA_(1-a-b)Pb(Cl_xBr_yI_(1-x–y))₃ configuration has been considered to predict the impact of composition on the bandgap by applying various machine learning models. Similarly, for Sn-based perovskite solar cells, we have investigated Cs_aFA_bMA_(1-a-b)Sn(Cl_xBr_yI_(1-x–y))₃ configuration to precisely make the bandgap prediction. The machine learning models are applied for both the configurations by considering 80:20 ratio for trained and tested datasets. For Pb-based perovskite solar cells, the neural network model predicted the bandgap with highest accuracy, whereas the ExtraTreeRegressor model performed best for predicting the bandgap of Sn-based perovskites. These findings demonstrate the potential of machine learning to accelerate the development of high-efficiency, cost-effective perovskite materials, offering a transformative approach for the photovoltaic industry in its shift toward next-generation solar technologies.

We propose a ring core photonic crystal fiber (RC-PCF) which can transmit 274 orbital angular momentum (OAM) modes in the C and L telecommunication bands (1.52 µm to 1.61 µm). It consists of a chalcogenide ring core (As₂S₃) and silica (SiO₂) cladding. The fiber has a segmented air hole geometry with gradually increasing air holes radii. This design has been numerically investigated using COMSOL Multiphysics. Several key parameters, including mode purity, confinement loss, effective mode area, numerical aperture, and nonlinear coefficient, have been calculated. The fiber exhibited a high mode purity (greater than 95%) for all supported modes and low confinement losses in the range 10^–10–10^–11 dB/m. The optimized design has produced a high numerical aperture in the range 0.25–0.34 and has a low nonlinear coefficient in the range 0.11–0.20 W⁻¹ km⁻¹ for stable transmission.

This research introduces a sophisticated computational methodology that combines DFT, SCAPS-1D simulations, and machine learning to enhance the development of lead-free Sr₃BiBr₃ perovskite solar cells (PSCs). DFT simulations indicate that Sr₃BiBr₃ possesses a direct bandgap of 1.44 eV, elevated absorption coefficients, and remarkable stability, making it an excellent choice for solar energy applications. SCAPS-1D simulations were utilized to evaluate device performance by examining different electron transport layers (ETLs), including WS₂, C₆₀, SnS₂, and IGZO, as well as hole transport layers (HTLs) such as CuI, CFTS, and Cu₂O. Among the configurations evaluated, the pairing of WS₂ as ETL and Cu₂O as HTL attained the maximum power conversion efficiency (PCE) of 30.18%, while the configurations utilizing CuI and CFTS exhibited PCEs of 27.44% and 23.52%, respectively. Additionally, three machine learning models, Random Forest (RF), Gradient Boosting (GB), and Decision Tree Regressor (DTR), were used to forecast the optical performance of PSCs based on 10,989 SCAPS-1D simulated datasets. The models were trained on 80% and tested on 20% of critical PSC parameters, with prediction accuracy evaluated using error measures such as RMSE, MSE, MAPE, and R². Of the three, RF attained the highest accuracy (RMSE = 0.0779, R² = 0.9973), surpassing both GB and DTR. SHAP analysis indicated that defect density, interface defects, and acceptor density were the predominant factors affecting PCE. The RF model exhibited significant predictive accuracy, great generalization, and efficient feature importance assessment, establishing it as the most dependable approach for projecting PSC efficiency.

We propose a method, based on the Mountaineering Team-Based Optimization (MTBO) algorithm, to extract the small-signal model parameters of a GaN HEMT. Traditional optimization algorithms, like Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO), tend to remain trapped in local optima and have slow convergence speeds during parameter extraction. To overcome these limitations, dynamic parameter control strategies are introduced by the MTBO algorithm. Four key mechanisms are governed by these strategies, including Coordinated Mountaineering, Disaster Response, Synergized Disaster Resilience, and Member Replacement. Both global search capability and convergence stability in the optimization process are significantly enhanced by this approach. A GaN HEMT small-signal equivalent circuit model is constructed, with the intrinsic parameters extracted and optimized through the application of the MTBO algorithm. A comparative analysis is conducted using the PSO and GWO algorithms. Experimental results show that, within the frequency range of 0.5–20.5 GHz, the MTBO algorithm outperforms the PSO and GWO algorithms in both S-parameters fitting accuracy and convergence speed, providing a more accurate representation of the device characteristics.

In this paper, we propose a simulator and methodology for predicting the program threshold voltage (V_t) distribution in charge trap-based 3-D NAND flash memory, considering z-direction interference (Z-interference) induced by random grain boundaries (GB) within the polycrystalline silicon (poly-Si) channel. Most previous studies have modeled Z-interference by fixing the GB or have investigated the V_t distribution without including Z-interference arising from random GB characteristics. However, there is a lack of research analyzing Z-interference in the program V_t distribution that results from random GB characteristics. Consequently, electrical characteristics corresponding to cell variation and GB variation were trained into a machine learning model through Technology Computer-Aided Design (TCAD) simulations to comprehensively analyze Z-interference and Vt distribution formation in victim (Vic) cells influenced by additional factors determined by random program verify (PV) levels of aggressor (Agr) cells, thereby performing Monte Carlo simulations on random strings. The proposed simulator enables prediction of the Vt distribution with Z-interference under both random and specific GB conditions, suggesting that precise control of grain boundaries during the fabrication process can establish useful design guidelines for process optimization in 3-D NAND flash memory.

Herein we have demonstrated a comprehensive analysis of the electronic structure and transport properties of the (5, 5), (7, 7), (11, 11) CNTs encapsulated with the simple sulfur chain and helical sulfur chain. Electronic structure calculation and geometric parameter evaluation clearly depict that compared to the simple chain, the helical sulfur chain has a huge impact on the (5, 5), (11, 11) CNTs and a substantial charge transfer event occurs between these moieties. Projected density of states (PDOS) and band structure calculation confirms that both simple and helical sulfur chains couple with the conduction bands of CNTs and alter the conduction band by introduction of orbitals from the sulfur chain level. From the current–voltage curve, we can argue that up to 1 eV voltage bias, the helical sulfur chain encapsulated composite showed higher current transport and significant enhancement of quantum conductance is achieved for a (5, 5) CNT-helical sulfur composite. Hence both a simple sulfur chain and helical sulfur chain could be an active element tuning the electronic and transport properties of carbon nanotube-based nanocomposite.

Lithium-ion batteries are the power plants of our digital age, supplying energy to smartphones, laptops, electric vehicles, and energy storage systems. However, temperature significantly affects their performance. In this study, a cylindrical lithium-ion battery was simulated within an air-flow cooling chamber to investigate temperature rise and cooling performance. Key battery parameters such as voltage, current, power, and temperature were analyzed under four C-rates: 2.5, 3.5, 4.5, and 5.5. The simulation results show that the maximum temperature rise (T_Maximum) increases from 5.72 K at 2.5C to 20.36 K at 5.5C, while the average temperature rise (T_Average) and minimum temperature rise (T_Minimum) vary from 5.59 K and 5.25 K at 2.5C to 21.79 K and 20.47 K at 5.5C, respectively. Additionally, the voltage range expands with increasing C-rate, from 3.52–3.96 V at 2.5C to 3.38–4.20 V at 5.5C, and the output power reaches its peak negative value at the highest C-rate. These results quantitatively demonstrate the effect of C-rate on thermal and electrochemical performance, providing essential data for designing effective battery thermal management strategies.