Journal of Computational Electronics最新文献

In this study, we explored the optimal performance of perovskite solar cells (PSCs) using the tin-halide material Rb₂SnI₆. This study focuses exclusively on the electrical properties of the devices, as simulated using SCAPS-1D software (solar capacitance simulator). The SCAPS-1D was employed to improve the device in the Rb₂SnI₆-based PSC, which utilized tungsten disulfide (WS₂) as the electron transport layer and cadmium telluride (CdTe) as the hole transport layer (HTL). To identify the most suitable electron transport layer (ETL), we initially investigated WS₂, SnS₂, PCBM, and C₆₀. The ITO/WS₂/ Rb₂SnI₆/CdTe/Ni structure proved to be the most effective ETL after extensive investigation, demonstrating a power conversion efficiency (PCE) of 24.95%, a Voc of 1.0896 V, a Jsc of 44.6795 mA cm², and an FF of 82.71%. Subsequently, we evaluated the impact of the absorber thickness, ETL thickness, and defect density on the device’s effectiveness in the Rb₂SnI₆, WS₂, and CdTe layers. We further investigated the effect of adjusting the interfacial defect densities at the CdTe/Rb₂SnI₆ and Rb₂SnI₆/WS₂ interfaces to optimize the device’s capabilities further. Additionally, we examined the proposed PSC’s quantum efficiency (QE), current density–voltage (J-V), shunt resistance, series resistance, capacitance–voltage, working temperature, and generation-recombination parameters. The results of these simulations provide valuable information for the excellent scientific fabrication of an inorganic PSC that is based on Rb₂SnI₆.

A high performance Kretschmann configuration-based surface plasmons resonance (SPR) biosensor is proposed for the detection of hepatocellular carcinoma (HCC) liver tissues. The proposed structure consists of calcium fluoride (CaF₂) prism, silver (Ag) metal, and a heterojunction of titanium dioxide (TiO₂), lead titanate (PbTiO₃), and molybdenum di selenide (MoSe₂). Role of constituents materials is analyzed in terms of their contribution towards enhancement in the performance. At near infrared wavelength of 1000 nm, the thickness and number of layers of constituent layers is optimized in the light of practical realization. The proposed biosensor provides an ultrahigh sensitivity of 486 deg/RIU with a full-width half maximum (FWHM) of 1.0720 degrees and a figure of merits (FoM) of 453.35 RIU⁻¹. Further, the corresponding power-loss ratio is also calculated. Hence, the combined performance factor for the proposed sensor is 480.56 RIU⁻¹. The novelty of the work relies in the design and selection of material (especially TiO₂ and PbTiO₃) that offers the highest possible values of performance parameters for prism based sensor in the best of our knowledge.

Based on simulation, this work introduces the single-event burnout (SEB) results of P-GaN gate AlGaN/GaN high electron mobility transistors (HEMTs) and proposes a hardened structure with a PN junction connected to the drain in the buffer layer. The simulation results indicate that the SEB mechanism of P-GaN gate AlGaN/GaN-HEMTs is mainly related to the charge enhancement and the impact ionization process dominated by the high-field region near the drain. Electrons in the high-field region between the gate and drain can gain sufficient energy and generate electron–hole pairs in the high-field region near the drain during the collection process. The avalanche ionization process triggered by these electrons leads to a rapid increase in the electric field, ultimately causing the peak electric field at the drain side to exceed the critical electric field of the material, resulting in SEB. The proposed hardened structure (H-HEMT) effectively improves the SEB threshold voltage by improving the electric field distribution near the drain. Under the condition of linear energy transfer (LET) of 0.6(pC/mu m) with heavy ion normal incidence, the SEB threshold voltage of the conventional structure (C-HEMT) is 230 V, while the H-HEMT can reach 420 V, showing better SEB resilience.

Graphene nanoribbon (GNR) is emerging as a superior material for nanometer-scale interconnects, offering superior performance compared with traditional copper materials. To date, most research on GNR interconnects has focused on voltage-mode signaling (VMS) scheme, with little study on current-mode signaling (CMS) scheme. In this paper, we propose an equivalent circuit model of two-wire coupled multilayer graphene nanoribbon (MLGNR) interconnects using VMS and CMS schemes. Moreover, the model takes into account influence of temperature effect, coupling capacitive and mutual inductive. Performance of victim wire in two-wire coupled MLGNR and Copper (Cu) interconnects using VMS and CMS signaling schemes is investigated by applying the decoupling approach and ABCD parameter matrix method at local, intermediate, and global levels, respectively. In addition, the performance of MLGNR and Cu interconnects employing VMS and CMS systems is thoroughly compared and examined in this research. The results reveal that interconnects adopting the CMS scheme have less output voltage swing, less crosstalk delay, greater 3-dB bandwidth, and better signal integrity, compared to interconnects applying the VMS scheme, under the same conditions. With respect to noise, we observe that the CMS scheme has lower noise amplitude, smaller noise peak, and smaller noise width, resulting in greater noise immunity. Moreover, it is manifested that crosstalk delay, noise width, and 3 dB bandwidth are all temperature-dependent. As the temperature rises, both the delay and noise width increase, while the bandwidth decreases. In addition, the results indicate that MLGNR interconnects exhibit lower crosstalk delay, narrower noise width, larger bandwidth, and smaller dynamic power consumption compared to Cu interconnects under the same conditions. Furthermore, we discuss performance optimization methods for interconnects using both VMS and CMS schemes. Also, it is discovered that there is great agreement between the results of HSPICE simulations and those produced by the ABCD parameter matrix technique.

Adsorption of four canonical, two methylated, and one mutated nucleobases have been studied on single-layer blue phosphorene (SL-BlueP), including van der Waals interactions within density functional theory. Our calculations for electronic charge transfer demonstrate that all the considered bases undergo physisorption on SL-BlueP with a charge transfer within the range of -0.004 to + 0.024 |e|. The work function of SL-BlueP decreases by 0.08, 0.10, and 0.19 upon adsorption of adenine, cytosine, and guanine, respectively, and its bandgap can be shrunk by as much as 36%. Interestingly, the current–voltage (I-V) curves show characteristic responses depending on the type of nucleobases. Furthermore, the adsorption of nucleobase molecules on SL-BlueP gives rise to distinct energy loss spectra. The obtained distinguishable features may be used for ultraselective detection of DNA nucleobases.

Half-Heusler compounds hold great promise for thermoelectricity due to their excellent thermal stability and electronic transport properties. This study unveils the physical characteristics of half-Heusler compounds XCoSi (X = V, Nb, Ta) as potential materials for thermoelectric using the Quantum ESPRESSO and PHONOPY codes with PBEsol-GGA correlation functional. The electronic band structure calculations revealed the semiconducting nature of the compounds with an indirect band gap (X (rightarrow ) W) of size 0.55 eV, 0.84 eV, and 1.25 eV for VCoSi, NbCoSi, and TaCoSi, respectively. The XCoSi(X=V, Nb, Ta) compounds demonstrate dynamic and mechanical stability, with ionic bonds and predicted ductility of these alloys. Additionally, critical parameters for thermoelectric application are computed, including the Seebeck coefficient (S), electrical conductivity ((sigma )), thermal conductivity ((kappa )), and the figure of merit (ZT). At room temperature, both p-type and n-type XCoSi (X = V, Nb, Ta) exhibit figure of merit values close to unity: 0.96 for VCoSi, 0.98 for NbCoSi, and 0.99 for TaCoSi, based solely on the electronic contribution to thermal conductivity. Including the lattice thermal conductivity provides a more accurate assessment of the thermoelectric potential of XCoSi (X = V, Nb, Ta). Among them, VCoSi shows greater potential for thermoelectric applications compared to TaCoSi and NbCoSi.

The implementation of a neural network on very large-scale integrated (VLSI) circuits provides flexibility in programmable systems. However, conventional field-programmable gate array (FPGA) neural chips suffer from longer computation times, higher costs, and greater energy consumption. On the other hand, multilayer perceptron (MLP) network implementation over VLSI exhibits increased speed with a smaller chip size and reduced cost. This work aims to implement an MLP neural network using double-gate metal oxide semiconductor field effect transistors (DGMOSFETs) functioning as neurons. The suggested network architecture is offered as a package utilizing very high-speed integrated circuit hardware description language (VHDL). The weights of the MLP are obtained by training a neural network with electrocardiogram (ECG) signals taken from the PhysioNet database. The ECG input signals, obtained weights and bias, are given to the designed MLP for testing. The classification accuracy of this trained neural network is 94.48%. A power analysis is also conducted for the hardware-designed MLP to validate the power reduction performance. In terms of speed, the required number of components and power, the performance of this design employing DGMOSFET outperforms its single-gate MOSFET (SGMOSFET) counterpart.

The modern electronic devices’ development heavily relies on the miniaturization of MOSFET transistors. On the other hand, reduction in transistor sizes will face significant challenges, like short-channel effects. To enhance transistor performance, it is essential to explore and utilize new materials. Black phosphorene has emerged as a promising material for constructing transistors and other electronic components. Accurate modeling is crucial for predicting the behavior of future nanoscale transistors. One of proposed simulation methods is the top-of-barrier model. This study analyzes transistors based on black phosphorene nanoribbons. The electronic structure of these nanoribbons is calculated using the tight-binding method with up to five nearest neighbors. The top-of-barrier computational approach within the Landauer framework is employed to determine device characteristics. Initial evaluations of a structure without antidots show that creating an off-center antidot increases the on current to 4.98 mA. The threshold voltage also rises by 0.2 V, indicating an increase in the energy band gap, which reduces the off current significantly. The on/off current ratio can be improved by up to 2500 times with an optimal antidot design. Non-central antidots do not significantly affect the threshold voltage or off current.

This manuscript investigates how magnetic and non-magnetic effects influence the firing patterns, oscillations, and synchronization properties of the Hindmarsh–Rose neuron model under different magnetic conditions. The development of a fractal–fractional Hindmarsh–Rose neuron model is proposed for investigating self-similarity across different scales to analyze and understand the complexities when extreme magnetic flux varies and reaches its critical value. The mathematical modeling of the Hindmarsh–Rose neuron model is established under an application of the Caputo–Fabrizio and Atangana–Baleanu fractional differential operators. For the sake of numerical simulations via the Adams–Bashforth–Moulton method, the discretization of spatial and time domains on fractal–fractional derivatives is employed to generate numerically powerful schemes within approximate accuracy. For understanding the brain function and neural oscillations, the magnetized and demagnetized Hindmarsh–Rose neuron model revealed suppressed neuronal activity and the effects of transcranial magnetic stimulation. Our results suggested two aspects: one is trapping of neurons, striking phenomena and firing patterns under demagnetization, while the other is neurological disorders, spiking and bursting in neurons based on neural interfaces under demagnetization.

The performance of 2D material-based field-effect transistors (2D FETs) is significantly influenced by the vertical extension, or depth, of electrostatically doped side Schottky contacts, which is determined through etching. This study employs TCAD modeling to compare back-gated FETs with varying source/drain contact depths and channel lengths. Results indicate that deeper side contacts hinder electric field crowding at the metal/channel interface, resulting in wider Schottky barriers, diminished carrier tunneling, and reduced on-state current. In contrast, introducing a low-k dielectric beneath the source and drain yields the opposite effect. Therefore, in the development of industry-compatible 2D FETs, the depth and design of side contacts must be carefully optimized, as they are critical factors in achieving low-contact resistance devices.