Journal of Computational Electronics最新文献

Malaria remains a major global health challenge, with millions of cases and significant mortality each year, particularly in tropical and subtropical regions. Conventional diagnostic techniques, such as microscopy and rapid diagnostic tests (RDTs), suffer from limitations including reagent instability and false negatives. This necessitates rapid and reliable diagnostic alternatives for effective management and control. Surface Plasmon Resonance (SPR) based biosensors are now widely recognised for detecting biomolecular interactions in real time, without the need for labelling. In this work, we present the design and numerical analysis of a high-performance Kretschmann-configured surface plasmon resonance-based sensor. The proposed structure incorporates silver (Ag) as a metal layer, silicon (Si) as a dielectric layer and ultrathin TiO₂ as an additional dielectric layer to enhance optical response for medical diagnostics. The sensor is evaluated for the detection of malaria-infected red blood cells across different intraerythrocytic developmental stages. Performance metrics were analysed using the finite element method (FEM) in COMSOL Multiphysics and the transfer matrix method (TMM) in MATLAB environment. Optimization of thickness and layer configuration yielded sensitivity of 425 deg/RIU, 337.03 deg/RIU, and 302.70 deg/RIU for ring, trophozoite and schizont phases, respectively. Corresponding figure of merit (FoM) was calculated as 124.70 RIU⁻¹, 109.03 RIU⁻¹ and 101.95 RIU⁻¹ with a maximum detection accuracy of 0.336 deg⁻¹ for the schizont phase. Furthermore, a tolerance study was done to assess the robustness against minor fabrication errors. These results demonstrate the potential of the suggested SPR sensor, which utilises enhanced plasmonic responses to detect minimal refractive index variations, as a platform for early malaria diagnosis.

Parameter extraction for the triple-diode photovoltaic (PV) model presents a complex, highly nonlinear optimization problem. It necessitates an optimization algorithm that has strong exploration and exploitation abilities to avoid getting stuck in local optima and to accurately determine the model’s parameters. Although various optimization methods have been used in the literature to address this problem, most of them produce poor results, show instability across different PV modules, have slow convergence, and/or require high computational costs. These limitations motivate us to introduce a new robust parameter identification technique called IGO, which can achieve more accurate results with fewer function evaluations. It is based on integrating the recently published growth optimizer (GO) with two new optimization strategies—the convergence improvement strategy and the ranking-based update strategy. The latter strategy steadily enhances the exploration operator throughout the optimization to prevent premature convergence to local optima. Simultaneously, it gradually boosts the exploitation operator in the late phases to accelerate convergence to the global optimum. The former strategy focuses on enhancing the exploitation operator during the optimization process to maximize convergence speed while strengthening the exploratory operator in late stages to mitigate the risk of settling in local optima. Integrating both strategies in the proposed IGO aims to balance exploration and exploitation throughout different phases of iteration, thereby preventing stagnation in local optima and encouraging rapid convergence toward the global optimum. The proposed IGO is tested on six popular PV modules and compared with several recently published optimizers using various statistical measures in addition to convergence speed to demonstrate its effectiveness and significance. The experimental results demonstrate that IGO outperforms all other methods in both parameter quality and convergence speed, confirming it as a reliable alternative for extracting the unknown triple-diode model parameters.

This study presents a real-time biosensor for hemoglobin concentration detection based on optical Tamm resonance in a one-dimensional photonic crystal platform. The sensor architecture, comprising a silver layer, a blood sample layer, and a periodic Si/SiO₂ multilayer on a BK7 glass substrate, supports sharp resonance features due to Tamm state excitation at the metal–dielectric interface. Notably, the biosensor differentiates blood groups (A, B, and O) through distinct resonance wavelength shifts arising from their characteristic refractive index differences, enabling group-specific hemoglobin detection. The resonance dip initially appears at 751.1 nm and exhibits a pronounced redshift with increasing hemoglobin concentration, reaching up to 814.2 nm for 200 g/L in blood group O. Using the Transfer Matrix Method, the optical response is evaluated under both normal and oblique incidence for TE and TM polarizations. The biosensor demonstrates exceptional performance under TE-polarized oblique incidence, achieving a maximum sensitivity of 0.599 nm/(g/L), a figure of merit of 0.716 1/(g/L), and a detection limit as low as 0.069 g/L. The prism-free, planar configuration ensures a compact and cost-effective setup ideal for point-of-care diagnostics. Additionally, the device maintains high spectral resolution and stable operation across physiological temperature ranges, making it a robust platform for real-time clinical blood analysis.

The present work comprehensively investigates the design, optimization, and predictive modeling of a high-performance, lead-free RbGeI₃-based perovskite solar cell employing WS₂ as the ETL and CuI as the HTL. Numerical simulations performed using SCAPS-1D were validated against theoretical efficiency limits and electrostatic consistency checks, confirming the model’s physical reliability. The device achieved an optimized η of 24.15%, with V_oc = 1.1184 V, J_sc = 25.999 mA·cm⁻², and FF = 83.09%, demonstrating excellent charge extraction and minimal recombination losses. Systematic parametric analyses revealed the critical influence of absorber doping, transport layer defect density, resistive losses (R_s/R_sh), absorber thickness (t-Abs), defect density (Nt), temperature, and solar irradiance on overall performance. Optimal operation was achieved for Nt = 10¹⁴ cm⁻³, where light absorption and carrier transport are well balanced. Furthermore, machine learning (ML) algorithms, including XGBoost, random forest, and gradient boosting, were employed to predict photovoltaic outputs with near-perfect accuracy (R² ≈ 1.0). The XGBoost model successfully identified absorber defect density, series resistance, and illumination intensity as the most dominant performance-determining features. The results demonstrate that the synergistic combination of WS₂/CuI transport layers and ML-guided optimization establishes a promising framework for stable, efficient, and eco-friendly RbGeI₃-based PSCs, paving the way for next-generation lead-free photovoltaic technologies.

Quantum cellular automata (QCAs) are a promising alternative to traditional CMOS technology due to their lower power consumption and ability to function at the nanoscale. However, challenges such as fault tolerance and energy efficiency remain, especially for arithmetic circuits like multipliers. The Vedic multiplier, known for its reduced computational complexity, presents a valuable opportunity to address these issues. By implementing fault-tolerant mechanisms within the QCA architecture, we aim to improve the reliability and performance of n x n multipliers in critical applications, such as cryptography, signal processing, and neural network accelerators. The proposed Vedic multiplier is designed using a hybrid of Urdhva Tiryakbhyam Sutra (vertical and crosswise technique) and error-correcting QCA gates to ensure fault tolerance. The design is implemented in a hierarchical manner, utilizing optimized QCA logic gates to form the partial product generation and summation stages. Error detection and correction techniques, such as cellular redundancy and parity-based correction, are embedded within the architecture to ensure resilience against cell misalignment and tunneling errors. Power consumption is minimized by optimizing the layout to reduce wire crossings and cell interactions. The energy efficiency and fault tolerance of the design are evaluated using QCADesigner. Simulation results demonstrate that the proposed Vedic multiplier achieves a 30% reduction in power consumption compared to conventional QCA multiplier designs. Fault tolerance is improved, with the system being able to detect and correct up to 95% of single-cell faults during operation. The delay is minimized by 20%, ensuring high-speed performance. Additionally, the energy dissipation per computation is found to be 8.5 aJ (attojoules), making the design highly energy efficient for nanoscale applications. The proposed Robust and Energy-Efficient Fault-Tolerant n x n Vedic Multiplier offers significant improvements in power efficiency and fault tolerance, making it ideal for next-generation QCA-based systems. The Vedic multiplier's inherent simplicity, combined with advanced error correction mechanisms, enables reliable and high-performance multiplication operations at the nanoscale. These results highlight the potential of QCA for applications requiring energy-efficient and fault-resilient computing systems, such as cryptography, machine learning, and low-power IoT devices.

Fractal structures are natural patterns that repeat themselves. They have several unique features that make them ideal for solar energy absorption and sensing applications. In this study, we present a high-performance, polarization insensitive solar absorber comprises of a nickel (Ni)-made hash-shaped fractal geometry develop over a thin layer of gallium-doped zinc oxide (GZO) features a high absorption rate that covers the visible and near-infrared wavelengths of the spectrum. The results show that broadband aggregative absorptivity of 92% is attained between 380 nm and 3850 nm attributed to remarkable localized surface plasmon resonance (LSPR) induced by the periodic array of Ni nano-resonators and surface plasmon resonance (SPR) at the interface of GZO-SiO₂ layers. Further, the absorptivity remains above 90% from 670 nm to 3850 nm over a bandwidth of 3180 nm. With the utility of high-temperature resilient materials in the developed metamaterial structure, it shows potential for the thermal applications; as the results indicate the maximum heat radiation efficiency is 92.88% at 1600 K. Aside from that, we provide insight into the broadband high solar light capturing characteristics of the proposed device with the support of surface current density and electric field distribution study at the selective wavelengths. Furthermore, the device’s parametric study revealed a minor impact on its absorptivity/emissivity characteristics while also suggesting its robustness, which could be useful in device manufacture process. The overall benefits of the proposed device show its potential for high-temperature solar energy harvesting applications and solar thermophotovoltaic (STPV) cells.

An in-plane high-g microelectromechanical systems (MEMS) accelerometer is designed for ultra-large acceleration measurements. The proposed device is a piezoresistive MEMS accelerometer, in which piezoresistors are employed as signal transduction elements. Unlike conventional cantilever-beam-supported structures, the proof mass is supported by four suspended thin plates to enhance structural stiffness and reliability under high-g shock loading. The sensing structure is bonded to the substrate using bonding technology. The accelerometer adopts an in-plane sensing configuration, where the applied acceleration is perpendicular to the surface of the proof mass. This configuration effectively avoids large shear stresses at the bonding interface under extreme acceleration, thereby improving the mechanical robustness and service life of the device in high-g applications. Four piezoresistors are symmetrically fabricated at the roots of the suspended thin plates to convert structural deformation into resistance variations. The feasibility of the proposed design is validated through theoretical analysis and finite element method (FEM) simulations, and an optimal structural design is obtained. Simulation results indicate that the measurement range of the accelerometer can reach 100,000 g, while the overload resistance can reach 200,000 g. The optimized dimensions include a proof mass side length of 1000(mu m), suspended thin plates with a length of 600(mu m) and a width of 320(mu m), a structural thickness of 80(mu m), and a gap of 5(mu m)between the lower surface of the structure and the substrate. The results demonstrate that the proposed accelerometer is suitable for high-g acceleration measurement applications

We study (Ag:HfO₂), designed to act simultaneously as an antireflective coating (ARC) and an intermediate reflective layer (IRL) in planar amorphous silicon (a-Si) solar cells. The optical behavior is analyzed using Scilab-based simulations with the Transfer Matrix Method (TMM), enabling precise modeling of light propagation and interference within multilayer structures. Silver incorporation modifies the HfO₂ permittivity via free-carrier effects described by the Drude model, producing epsilon-near-zero (ENZ) conditions and regions with negative permittivity. These properties enhance light trapping and absorption by minimizing front surface reflection and boosting internal reflection at the rear interface. The proposed planar approach improves optical absorption and internal quantum efficiency (IQE) without requiring complex nanostructures, offering a scalable, fabrication-compatible strategy for high-efficiency thin-film solar cells.

This research presents a surface plasmon resonance (SPR) sensor based on photonic crystal fiber (PCF), specifically designed for the detection of diabetes. Gold is used as the plasmonic material in a layered configuration to enhance sensor performance. The proposed design is analyzed using the finite element method (FEM) to assess its capability in identifying diabetes-related variations. The PCF structure features two rings of air holes organized in a hexagonal pattern, with a thin layer of gold plating applied to enable SPR excitation. SPR occurs when the surface plasmon polariton (SPP) mode and the fundamental core mode interact under phase-matching conditions. Diabetes-specific samples, characterized by distinct refractive indices (RI), are filled into the fiber. Variations in RI cause shifts in the SPR resonance wavelength observed through confinement loss analysis. The resonance shift between normal and diabetic samples reflects their differing RI values. The sensor attains a sensitivity of 2400 nm/RIU, based on these spectral shifts. With its straightforward sensing mechanism, the proposed PCF-based SPR sensor offers a practical, economical approach to diabetes diagnosis.

A novel structure of Low Barrier Schottky contact Super Barrier Rectifier (LB-SSBR) is proposed by introducing a partial SiGe region in the device’s anode side, creating a low electron-barrier structure. This low-barrier structure ensures that LB-SSBR can appropriately thicken the oxide layer without affecting the forward conduction characteristics, thereby improving the Single Event Gate Rupture (SEGR) tolerance. The TCAD simulation results show that, under the condition of no increase in reverse leakage current, the SEGR tolerance of LB-SSBR is significantly stronger than that of conventional SSBR. When heavy ions are incident from the most sensitive position of the device, the maximum electric field inside the oxide layer of LB-SSBR is 7.22 MV/cm, which is 46.08% lower than that of SSBR. Additionally, there is also a certain degree of improvement in both forward conduction and reverse recovery characteristics. The forward conduction voltage of LB-SSBR has decreased by 13.44%, and the reverse recovery charge of LB-SSBR has reduced by 38.34% compared to SSBR. In addition, the existing molecular-beam epitaxy (MBE) process can achieve the epitaxy of SiGe on Si substrate, making it convenient to prepare LB-SSBR structures.