Journal of Computational Electronics最新文献

Displacement and naturel frequency are the most important design parameters for diaphragms based microelectromechanical system (MEMS) pressure sensors. For nonconventional diaphragm design of MEMS devices, finite element method (FEM)-based analysis to obtain these two parameters requires quite long time and cost as compared to conventional diaphragm design including circular, square, and rectangular shape. Thus, one major disadvantage of FEM is the excessive time required for simulation. Machine learning (ML) algorithms might be an alternative approach to FEM analysis. ML algorithms, which is an easier, functional, and time and cost saving, might provide rapid prediction of essential information comprising displacement and naturel frequency of MEMS diaphragm design with accurate and reliable results. In this study, ML algorithms including XGBoost regressor, LightGBM regressor, CatBoost regressor, and TabNet regressor were used to estimate displacement (µm) and frequency (Hz) of perforated low temperature co-fired ceramic (LTCC) diaphragms using 200 FEM-based numerical results. Predicted results were compared by considering R², MAE, RMSE, and MAPE metric. According to these results, best performance was obtained by CatBoost regressor with the values of R² = 0.927 and R² = 0.995 for the displacement and frequency prediction, respectively. It was realized that CatBoost strikes an exceptional balance between computational efficiency and predictive performance, while LightGBM emerges as a strong alternative for scenarios prioritizing speed and memory efficiency. As a result, it was concluded that ML algorithms might be a useful, cost, and time effective tools for rapid analysis of displacement and naturel frequency of perforated diaphragms without requiring FEM analysis.

A Quantum Dot Cellular Automata (QCA) is a nanotechnology-driven computing method that leverages quantum mechanical principles. This work demonstrates the efficient use of reversible logic gates in QCA-based systems, a crucial aspect of scalable and energy-efficient computer designs. In this research, we propose an energy-efficient, compact XOR gate and several common reversible logic gates. The paper further illustrates the use of these gates to construct essential components, such as a one-bit comparator and an arithmetic logic unit (ALU). The ALU, designed with Feynman and Toffoli gates, is capable of performing eight arithmetic and logical operations. We conduct a comprehensive evaluation of cell complexity, area efficiency, delay, area-delay product (ADP), and energy dissipation. Additionally, we compare the characteristics of the proposed circuits with prior efforts to highlight advancements and identify areas for further improvement in reversible computing paradigms. The recommended architecture enhances the performance of the XOR gate, reversible logic gates, one-bit comparator, and ALU. The XOR gate achieves a 67.86% reduction in cell complexity, an 85% improvement in area efficiency, and a 50% reduction in quantum cost. Feynman and Toffoli gates demonstrate a 75% reduction in area and an 87.5% reduction in quantum cost. For the one-bit comparator and ALU, the proposed solution reduces area by 87% and latency by 40%, saving both space and time. With a quantum cost that is 90% lower than traditional designs, the proposed architecture optimizes quantum circuits for real-world applications.

This study investigates the scaling behavior of the Dual Interbridge Tree-shaped Nanosheet FET (DIB-TreeFET) for sub-3 nm digital logic applications. Device-level simulations using Sentaurus TCAD explore the effects of varying interbridge thickness ((IB_{T})) from 10 nm to 30 nm and nanosheet thickness ((N_{T})) from 3 to 9 nm, while keeping other parameters constant. Increasing (IB_{T}) results in a 1.71 times improvement in (I_{ON}), and similarly, increasing (N_{T}) from 3 nm to 5 nm results in an enhancement in (I_{ON}) of about 1.58 times. However, both parameters also contribute to less pronounced threshold voltage roll-off, indicating stronger short-channel effects. Optimal device performance is observed at (IB_{T}) as 20 nm and (N_{T}) as 5 nm. A CMOS inverter built with this configuration is evaluated under varying V_DD, load capacitance (10–1000 aF), and input frequency (1–50 GHz). Key metrics, including propagation delay, power-delay product (PDP), and energy-delay product (EDP), are assessed. A tradeoff point at V_DD=0.575 V offers balanced performance. At V_DD=0.7 V, the inverter achieves noise margins of 0.29 V ((NM_{H})) and 0.32 V ((NM_{L})), with a voltage gain of 9.98, demonstrating its suitability for ultra-scaled low-power logic applications.

Acoustic sensing applications are being actively explored across a wide range of fields, including consumer electronics, biomedical devices, industrial applications, space technology, and military-grade equipment. In the past, the electret condenser microphone (ECM) was the primary technology used for sound detection. However, advancements in micro-electro-mechanical systems (MEMS) acoustic sensors have transformed the landscape. With the stabilization of MEMS manufacturing processes, these sensors are now increasingly integrated into mobile phones, wearable devices, Bluetooth headsets, hearing aids, digital cameras, automotive voice control systems, and environmental monitoring equipment. Developing silicon MEMS acoustic sensors may seem straightforward, but it involves addressing a range of complex and inherent challenges. This paper provides a comprehensive overview of the materials and technologies involved in the development of MEMS acoustic sensors. We discuss various sensing mechanisms, including piezoresistive, capacitive, piezoelectric, triboelectric, optical, and Spin-MEMS technologies. Additionally, we outline the design techniques used in sensor development. Furthermore, we explore AI-based methods to improve sensor sensitivity and examine the operational parameters of commercial MEMS microphones.

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.

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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.