Journal of Computational Electronics最新文献

Defect levels induced by defect-complexes in Ge play important roles in device fabrication, characterization, and processing. However, only a few defect levels induced by defect-complexes have been studied, hence limiting the knowledge of how to control the activities of numerous unknown defect-complexes in Ge. In this study, hybrid density functional theory calculations of defect-complexes involving oversize atom (indium) and n-type impurity atoms in Ge were performed. The formation energies, defect-complex stability, and electrical characteristics of induced defect levels in Ge were predicted. Under equilibrium conditions, the formation energy of the defect-complexes was predicted to be within the range of 5.90–11.38 eV. The defect-complexes formed by P and In atoms are the most stable defects with binding energy in the range of 3.31-3.33 eV. Defect levels acting as donors were induced in the band gap of the host Ge. Additionally, while shallow defect levels close to the conduction band were strongly induced by the interactions of Sb, P, and As interstitials with dopant (In), the double donors resulting from the interactions between P, As, N, and the host atoms including In atom are deep, leading to recombination centers. The results of this study could be applicable in device characterization, where the interaction of In atom and n-type impurities in Ge is essential. This report is important as it provides a theoretical understanding of the formation and control of donor-related defect-complexes in Ge.

The potential of surface plasmon resonance (SPR) biosensors to detect different biomolecules quickly and sensitively has attracted much attention. In this work, we use a numerical method to identify malaria phases by exploring the sensitivity adjustment of SPR sensors based on bimetallic, MXene and graphene layers. Effective treatment for malaria, a potentially fatal disease brought on by plasmodium parasites, depends on early identification. Innovative biosensing technologies are necessary since traditional diagnostic procedures frequently lack sensitivity and speed. The transfer matrix method is employed here in this study for reflectance calculation. The COMSOL software finds the electric field distribution across the various layers interfaces. The maximum sensitivity of 301.1667°/RIU has been attained for the proposed work.

The molecular field-coupled nanocompunting (molFCN) technology encodes the information in the charge distribution of electrostatically coupled molecules, making it an exciting solution for future beyond-CMOS low-power electronics. Recent literature has shown that multi-molecule molFCN enables the design of devices with tailored unconventional characteristics, such as majority voters working as artificial neurons. This work presents a multi-molecule molFCN neuron model based on the weighted-inputs formulation to estimate molFCN neurons behavior. Then, the introduced model is used to design each neuron of molFCN circuits working as neural networks. In particular, we propose a molFCN neural network operating as an input pattern classifier. The results show the model aptitude in predicting the logic output values for individual neurons and, consequently, entire networks. The model accuracy has been evaluated by comparing the results from the neuron mathematical model with those obtained from the circuit-level simulations conducted with the SCERPA tool. Overall, this study highlights the strategic use of diverse molecules in molFCN layouts, customizing circuit operations, and expanding design possibilities for specific molFCN device functioning.

In a mathematical sense, regardless of the equivalent circuit, solar cells are represented by nonlinear dependencies of current and voltage. First, this paper discusses novel mathematical formulations of the current–voltage dependencies of solar cells for single-diode, double-diode, and triple-diode models (SDM, DDM, and TDM, respectively). Second, for SDM, although the analytical solution expressed through Special Trans Function Theory (STFT) was known in the literature, in this paper, its applicability was checked for the first time, and a comparison was made with a literature approach. Third, for both DDM and TDM, novel original iterative procedures for current–voltage dependencies based on the application of the STFT have been proposed. Fourth, the accuracy and efficiency of all the proposed solutions were confirmed by observing two different, well-known solar cells. Furthermore, the applicability of the proposed solutions was confirmed by experimentally measuring the current‒voltage characteristics of solar cells under different climatic conditions. The proposed modeling approaches undoubtedly represent a new field for the application of STFTs.

Effective regulation of unmanned aerial vehicles (UAVs) is crucial to social and public safety. In this paper, a microwave UAV imaging method is proposed for multi-station polarimetric radars. A polarimetric far-field scattering model is built to formulate the inverse scattering problem for various multi-station radar configurations. (mathscr {L}_1)-norm regularization is incorporated in the inversion to realize a high spatial resolution. Numerical experiments are carried out with FEKO taking a typical quadcopter UAV as the target. Reconstruction results with polarization dependence of bistatic and multi-station radar configurations and multiple observation ranges are given. A spatial resolution study reveals the resolution of the proposed algorithm and analyzes the relationship between resolution and multiple factors. The results validate the feasibility of microwave UAV imaging with multi-station polarimetric radars.

Hybrid numerical methods show great potential in enhancing conventional approaches, particularly when dealing with complex structures beyond the capabilities of individual methods or standard software. This paper provides a comprehensive overview of the Method of Moments combined with the Generalized Equivalent Circuit (MOM-GEC) in electromagnetic modeling. Through comparative analysis with traditional numerical methods such as the Method of Moments, Finite Difference Time Domain (FDTD), and Finite Element Method (FEM), MOM-GEC’s unique advantages in adaptability, accuracy, and computational efficiency are highlighted. Mathematical formulations based on equations are integrated to clarify the method’s concepts and integration processes. The study showcases MOM-GEC’s successful deployment in various applications, demonstrating its versatility and efficacy in intricate scenarios such as antenna arrays, graphene-based metamaterial devices, and dosimetry in partially enclosed environments. Each case study undergoes re-evaluation by incorporating the generalized equivalent circuit approach, emphasizing MOM-GEC’s effectiveness in addressing diverse challenges. This underscores MOM-GEC’s versatility and efficacy across complex scenarios, reaffirming its value in electromagnetic modeling.

Titanium dioxide (TiO₂) has gained popularity especially in photovoltaic applications, owing to its transparency in the visible region, and scratch resistance. In this work, the potential of TiO₂ as a capping layer for c-Si p-type SiN_x passivated emitter and rear contact (PERC) solar cells is studied through extensive optical and device simulations. The bifacial PERC solar cell model used in this study is calibrated with an experimental device having an efficiency of 22.19%. Device simulation results show that TiO₂ deposited by the mesoporous technique outperforms atmospheric pressure chemical vapor deposition (APCVD) and atomic layer deposition (ALD)-based TiO₂ layers when capped over SiN_x (n = 2.1) passivated solar cells. Furthermore, it is shown that the efficiency of SiN_x (n = 2.1)/TiO₂ based solar cells is maintained, even when the TiO₂ layer thickness varies from 75 to 95 nm. To enhance the efficiency further, the type of SiN_x layer (characterized by the n value), and the thicknesses of SiN_x and TiO₂ layers are optimized simultaneously to find the best combination of these parameters. The best front side solar cell efficiency of 22.43%, is obtained when a stack of SiN_x(n = 1.99)/TiO₂ (t = 58/76 nm) is used. Similarly, a rear side efficiency of 16.59% is achieved when the rear side Al₂O₃/SiN_x stack is capped with mesoporous TiO₂. These efficiencies are 0.24 and 1.25% higher, respectively, when compared to the original SiN_x passivated PERC solar cell, demonstrating the prospective of using TiO₂ in encapsulant-free commercial photovoltaic applications.

Graphyne-based nanotubes are cylindrical structures made up of a single layer of graphyne that has been rolled into a tube. These one-dimensional structures are constructed from carbon atoms with both sp and sp² hybridization. The common graphene-based carbon nanotubes, and graphyne-based nanotubes including α-, β-, γ-, α2-, 6,6,12-, and δ-graphyne nanotubes are introduced. The atomic structures and electronic characteristics of these tubes are reviewed. The electronic band structures and density of states calculated by density functional theory are presented. The nanotubes with different types and chiralities display either metallic or semiconducting characteristics. The variety of structures and electronic properties of these nanotubes make them extremely hopeful for applications, especially in nanoelectronic devices such as field emission transistors, sensors, nanoelectromechanical systems, super capacitors, energy and data storage devices.

This research focuses on the design and optimization of a graphene-based microwave ablation applicator (MWA) for tumor treatment. The unique properties of graphene, such as high impedance at microwave frequency and tunable Fermi energy, make it an ideal candidate for inducing charge carriers through an electric field or chemical doping. The study further employs machine learning techniques, including support vector regression (SVR) and artificial neural networks (ANN) to predict the ablation zone. The applicator design incorporates a helix antenna element connected to a coaxial cable working at 2.45 GHz with a graphene sheet and T-ring attached to the outer conductor. The challenge of achieving a zero-Fermi energy is addressed by introducing defects to increase surface resistivity, resulting in an impedance close to the required value. The results show that the graphene-based applicator enhances the ablation zone, leading to efficient and controlled tumor treatment. To predict the ablation zone accurately, the study employs machine learning techniques, including SVR and ANN utilizing Taguchi method to reduce computational complexity. Large and round ablation zone is achieved using novel applicator. Further, the performance metrics, including root-mean-squared error and coefficient of determination (R²), are utilized to evaluate the predictive capabilities of the model and found to be optimum. The research demonstrates the potential of graphene in improving MWA treatment and highlights the importance of machine learning in optimizing MWA and predicting treatment outcomes.

Graphical abstract

Internet of things (IoT) has necessitated the development of indoor photovoltaics to enable a web of self-powered wireless sensors/nodes. We analysed a CsPbI₃ wide band gap perovskite for indoor photovoltaic application. An Indoor photovoltaic (IPV) device based on CsPbI₃ showed a theoretical efficiency of 51.5% at a band gap of 1.8 eV under indoor light-emitting diode (LED) illumination. This high efficiency is due to better capture of the narrow emission spectrum of LED by a high band gap CsPbI₃ absorber. Under low luminance of indoor light sources, there is a low density of photogenerated carriers, which increases the ratio of trapped electrons to photogenerated electrons. The low photogenerated carrier density lowered bulk recombination, and the high trapped electrons to photogenerated electrons ratio increases IPV sensitivity toward interfacial recombination. Finally, the device optimization strategies of the IPV device, distinct from outdoor illumination devices are highlighted.