Journal of Computational Electronics最新文献

Electronic and thermoelectric properties of Irida-Graphene nanoribbons (IGNRs) are significantly influenced by their edge configurations. This article presents a comprehensive computational study of the band structure, density of states (DOS), transmission function, and current–voltage (I-V) characteristics of zigzag and armchair edge IGNRs. Zigzag edge IGNRs (ZIGNRs) exhibit localized edge states, which introduce a Dirac point at the Fermi level, contributing to metallic behavior and enhancing the Seebeck coefficient. In contrast, armchair edge IGNRs (AIGNRs) show semiconducting behavior with a bandgap of approximately 2.4 eV. The thermoelectric performance of ZIGNRs is superior, with a higher Seebeck coefficient and electronic figure of merit (ZT_e) compared to AIGNRs. The maximum Seebeck coefficient for ZIGNRs is about 7 μV/K, while for AIGNRs, it is about 1.5 μV/K. The ZT_e for ZIGNRs is approximately 0.007, and for AIGNRs, it is about 0.005. These findings provide valuable insights into the design and optimization of IGNRs for advanced thermoelectric and electronic applications.

The optical properties of two-dimensional (2D) Janus MoSTe photodetectors under irradiation of polarized light have attracted tremendous attention recently due to their potential applications in low power consumption nanoelectronics and optoelectronics. By using the nonequilibrium Green's function method combined with density functional theory, we theoretically investigate the optical properties of various substitution-doped Janus MoSTe photodetectors. It has been demonstrated that the photocurrents along the armchair direction for all built devices exhibit a cosine-function-like behavior, and those along the zigzag direction present a sine-function-like relationship with the polarization angle θ under irradiation of linearly polarized light. The maximum photocurrents are in range from 3.06 ({text{a}}_{0}^{2}/text{photon}) to 16.82 ({text{a}}_{0}^{2}/text{photon}) among As substituted Mo, W substituted Mo, S substituted Te, Te substituted S, and Se substituted S of the Janus MoSTe photodetectors, apparently larger than the photocurrent of 0.61 ({text{a}}_{0}^{2}/text{photon}) for pure MoSTe photodetector, since the atomistic doping significantly reduce the structural symmetry of the photodetectors. Interestingly, a maximum extinction ratio of 4.26 × 10² has been observed in Janus MoSTe photodetectors with W substituted by Mo atom, implying the ultrahigh polarization sensitivity of the Janus MoSTe photodetectors. In addition, an obvious anisotropy between the armchair and zigzag directions of system has been observed, since the generated photocurrent along the armchair direction is much larger than that along the zigzag direction. Therefore, the 2D Janus MoSTe monolayer should be a good candidate material for future nanoelectronic and optoelectronic applications.

The shallow trench isolation-based laterally diffused metal–oxide–semiconductor (STI LDMOS) is a crucial device for power integrated circuits. In this article, a novel framework that integrates an optimal objective function, Bayesian optimization (BO) algorithm, and deep neural network (DNN) model is proposed to fully realize the automatic and optimal design of STI LDMOS devices. On the one hand, given the structure of the device, the DNN model in the proposed method can provide ultra-fast and highly accurate performance estimation including breakdown voltage (BV) and specific on-resistance (R_onsp). The experimental results demonstrate 98.68% average prediction accuracy for both BV and R_onsp, higher than that for other machine learning (ML) algorithms. On the other hand, to target the specified value of BV and R_onsp, the proposed framework can fully automatically and optimally design the precise device structure that simultaneously achieves the target performance with the optimal figure of merit (FOM) of the device. Compared to technology computer-aided design (TCAD), there is only a 0.002% error in FOM and a 2.83% average error in BV and R_onsp. Moreover, considering the training time of the DNN model, the proposed framework is 100 times as efficient as other conventional frameworks. Thus, this research provides the experimental groundwork for constructing an automatic design framework for an LDMOS device and opens new opportunities for accelerating the development of LDMOS technology in the future.

We present a new subthreshold analytical model for dual-material junctionless gate-all-around negative capacitance field-effect transistors (DM JL GAA NCFETs). The model accurately reproduces the electrostatic potential distribution, subthreshold current characteristics of the device, threshold voltage, and subthreshold slope. By solving the Landau–Khalatnikov (L–K) equation with Poisson’s equation, the model provides a precise analytical solution that aligns closely with numerical results. The impact of various parameters such as channel length, DM gate ratio, and ferroelectric layer thickness on the device subthreshold behavior is systematically analyzed. It is found that the strategic combination between the JL structure and NC effect can allow achieving enhanced device performance at the nanoscale level. The results demonstrate that the optimized DM JL GAA NCFET exhibits enhanced short-channel performance at nanoscale level, reduced subthreshold swing of 49 mV/dec, lower threshold voltage of 0.20 V, and reduced OFF-current of 1.5 × 10^–5 nA. Therefore, the proposed design framework strategy paves the way for designers not only to identify the appropriate DM gate configuration and the suitable ferroelectric material for the development of ultralow-power and high-performance nanoelectronic circuits.

In this paper, a novel multi-inner analyte channel photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor is proposed to analyse plasmodium falciparum parasitized human Red Blood Cells (RBCs) that leads to ague. The full vectorial finite element method (FV-FEM) is employed to investigate the key propagation characteristics of the proposed sensor, such as confinement losses, resonance conditions, sensitivities, resolutions, and their linearities. Metallic plasmonic layers of gold (Au) and silver (Ag) are utilised, with two distinct channel shapes being used (circular and square). There are two alternative scenarios reported to identify the phases of the plasmodium falciparum cycle (Ring, Trophozite, and Schizont) in RBCs. The maximum spectrum sensitivities for circular type analyte channels have been found to be 4500 nm/RIU and 4750 nm/RIU, with resolutions of (2.2 times 10^{-5}) RIU and (2.1 times 10^{-5}) RIU for y-polarized and x-polarized modes, respectively. The spectral sensitivities of the square-shaped analyte channel, on the other hand, are 5300 nm/RIU and 6250 nm/RIU, with resolutions of (2 times 10^{-5}) RIU and (1.6 times 10^{-5}) RIU for y-polarized and x-polarized modes, respectively.

Gallium oxide (Ga₂O₃) is an emerging and promising candidate for high-power and radiation-rich environments, such as space, thanks to its ultra-wide bandgap (~ 4.9 eV) and high critical electrical field (~ 8 MV/cm). Radiation in space, such as protons, alpha particles and heavy ions, can cause serious damage to electronic devices and even lead to permanent damage. However, assessing these devices' reliability and radiation hardness in space-like environments is often expensive and complex. In the present work, we utilize a technology computer-aided design (TCAD) simulation-based framework that uses the concept of non-ionizing energy loss (NIEL) to evaluate the displacement damage in electronic devices under particle irradiation. To assess the radiation tolerance of Ga₂O₃ diodes, first, a TCAD model for Ga₂O₃ Schottky barrier diodes (SBD) is developed and calibrated/benchmarked to an experimental device, followed by irradiation simulations. The results show that Ga₂O₃ SBD can withstand a 5 MeV proton fluence of ~ 10¹⁵ cm⁻² with no change in the forward current voltage (IV) characteristics. This value is significantly higher than that of 4H-SiC (~5 × 10¹³ cm⁻²) and Si (~1 × 10¹²) SBDs with the same ideal breakdown voltage - V_BR (1600 V), demonstrating the potential of Ga₂O₃ as a radiation-hard technology.

Solar cells can be represented by different n-diode models. The most commonly used models are single-diode (SDM), double-diode (DDM), and triple-diode (TDM). The SDM is the simplest and most widely used model with reversible current (I)-voltage (V) expressions. The DDM and TDM are more precise models, but only a few approximate analytical Lambert W approaches are available for current‒voltage (I–V) expressions in the literature. This paper presents approximate analytical invertible voltage‒current expressions (V–I) for DDM and TDM via a g-function. Moreover, this paper presents a new formula for calculating the root mean square error (RMSE) in voltage estimation based on the derived expressions. It also demonstrates the limitations of the Lambert W function and the numerical unsolvability of its solution through examples for these purposes. In addition, the paper discusses and tests analytical and iterative solutions for solving the g-function and provides the MATHEMATICA code for DDM and TDM V–I expressions via the g-function. Therefore, this paper confirms the effectiveness and accuracy of using the g-function in solar cell modeling.

Metasurfaces have garnered significant attention in recent years for their ability to manipulate electromagnetic (EM) wave propagation, owing to their high design flexibility, low profiles, and ease of fabrication. This study proposes the use of polarization-dependent anisotropic metasurfaces to manipulate the phase of orthogonal linearly polarized EM waves, enabling polarization multiplexing with distinct functionalities based on incident polarizations. Additionally, the proposed metasurfaces enable the generation of single pencil beams, multiple pencil beams, circularly and elliptically shaped radiation beams, offering versatile polarization manipulation capabilities. The radiation theory of planar array antennas was employed to predict the far-field patterns of the metasurfaces, demonstrating satisfactory agreement with simulated results and affirming the feasibility of the proposed method. The ability of focusing the incoming EM wave into a focal point or multi focal points and generating vortex beam carrying orbital angular momentum (OAM) under the incidence of orthogonal linearly polarized waves are also demonstrated by the proposed anisotropic metasurfaces. This proposed metasurfaces pave the way for the development of multifunctional metadevices capable of advanced EM regulation through polarization and phase modulations in free space, with potential applications in wireless communication, imaging, and radar systems.

In this paper, the characteristics of the fundamental mode of surface plasmons in a circular cylindrical three-layer graphene waveguide structure are investigated. By using Maxwell equations in the cylindrical coordinate system and applying the boundary conditions, the dispersion relation has been derived for the fundamental mode. In the proposed model, along with the electric field distribution in the waveguide, the effect of different model parameters on the dispersion curve has also been investigated. For instance, the effect of chemical potential, temperature and the separation between the first-second and second-third layers of the graphene has been shown and discussed in detail. Furthermore, the effect of chemical potential, temperature and separation between the first-second and second-third layers of the graphene on the propagation length and phase speed is also discussed.

The pursuit of increasingly efficient and cost-effective solar energy solutions has driven significant advancements in photovoltaic (PV) technologies over the past decade. Among these innovations, bifacial solar cells, which capture sunlight from both the front and back surfaces, with front surface texturing and rear-side optimization playing crucial roles, present a promising avenue for enhancing efficiency compared to conventional designs. The effectiveness of these cells, however, is largely dependent on the optimization of rear surface properties and the material characteristics employed. This study investigates into the pivotal role of surface texture, particularly on silicon wafers, in shaping key performance metrics such as open-circuit voltage, short-circuit current, fill factor, and overall efficiency. Given the complex interdependencies among these parameters, machine learning (ML) tools, specifically random forest regression models, have been utilized to decode these intricate relationships. The findings underscore the significance of surface texture in modulating reflectance from both the rear and front surfaces, which in turn influences the overall performance of the solar cells. By applying ML models, this research provides an improved understanding of the impact of surface characteristics, thereby offering valuable insights into the optimization of design and material selection for next-generation high-performance solar cells. This ML optimization study indicates that the pyramid structures with a height of 3 μm and a base angle of 62° can significantly reduce reflectance to 9% while maximizing solar cell efficiency to 23.61%, marking a substantial advancement over existing designs. This model achieves 75% accuracy on synthetic test data and 78% on experimental data reinforcing model’s applicability despite typical ML limitations in PV systems.