Journal of Computational Electronics最新文献

A direct current (DC) compact model for negative capacitance field-effect transistors (NCFETs) based on a metal-ferroelectric-insulator-semiconductor (MFIS) structure is proposed, considering the influence of deep-level interface trap states. To overcome the bottleneck problem of accurately and efficiently solving models, an explicit algorithm is developed, which is used to solve the complex Landau–Devonshire (LD) formula for the second-order phase transitions in physical models and the transcendental equation of trap density of states and surface potential. Compared with existing algorithms based on analytical surface potential, the new method does not require the numerical methods involving several iterations to obtain more accurate results, and the model can accurately reflect the opposite control effect of interface traps on surface potential and current under different ferroelectric (FE) thicknesses. The high precision of the model was verified through comprehensive numerical calculations and experimental data, indicating that the model can be effectively applied to circuit simulation design under low-power condition.

Density functional theory (DFT) has sparked intense interest in computational material predictions, especially in electronic band structure, optical dielectric functions, elastic moduli, and phonon calculations using non-local hybrid functionals. Using the first-principle-based calculations, a wide direct Γ-Γ bandgap E_g of 2.68 eV has been reported. Our partial density of states (PDOS) data also demonstrate that the substance exhibits metallic properties, based on the nonzero density of states at Fermi-level E_F. Still, what is more, our computational data show the orbital hybridization between Ge 4s² and S 3p⁴ electron states on the valence level, and a strong repulsive force occurs on both Ge and S p electron orbitals. The optical absorption coefficient calculated can reach up to 3 × 10⁵ cm⁻¹, indicating good material absorption. Our elastic information provided predicts substance ductility and ionic-covalency of the group IV-VI material. We have also added Vickers hardness and machinability index to our publication, for the sake of completeness. Finally, the slight system instability and weak coupling of the GeS₂ material have been observed, according to our phonon dispersion and density of phonon states plot.

This current study focuses on predicting the electronic and magnetic behaviors of WCl₃ monolayer when subjected to an external electric field. Unlike CrI₃, the WCl₃ monolayer displays a preference for an antiferromagnetic (AFM) ground state with an in-plane easy axis. This AFM state remains consistent across the entire spectrum (0–1 V/Å) of the external electric field. The indirect electronic band gap of the WCl₃ monolayer is predicted to be about 2.16 eV. Through our analysis, we’ve identified that the dominance of the valence band maximum and the conduction band minimum stems mainly from the ({d}_{{x}^{2}-{y}^{2}}) orbital (52% contribution) and the ({d}_{{z}^{2}}) orbital (97% contribution) respectively, attributed to the W element. The majority of electronic transitions related to the band gap arise due to these specific orbitals. Furthermore, the application of an external electric field can adjust the band gap to zero, prompting a transition from semiconductor to metal at an electric field intensity of E = 0.9 V/Å. Using mean field theory, we estimate the Neel temperature (T_N) of the AFM system to be approximately 356 K, a notably high value surpassing room temperature. Moreover, the application of an electric field demonstrates the potential to further elevate the Neel temperature, crucial for the functionality of high-temperature spintronic devices. Our comprehensive examination also delves into the magnetic anisotropy of the WCl₃ monolayer. The analysis of magnetic anisotropy energy (MAE) indicates that, contrary to the CrI₃ monolayer, the transition metal W significantly contributes to the system’s MAE, which is predicted to be − (3.44text{ meV}/text{W}). The magnetic easy axis aligns along the (x) direction (in-plane).

This work investigated a method for improving the efficiency of solar cells through the incorporation of carbon nanotubes (CNTs), which were used as the absorber layer of the solar cell. The CNTs were generated using plasma-enhanced chemical vapor deposition (PECVD). The use of the PECVD-generated CNTs in the absorber layer of the solar cell was found to increase the electrical conductivity due to the introduction of a large number of free charge carriers in the form of electrons and holes. We were thus able for the first time to estimate a relation between plasma variables and the efficiency of the proposed solar cell. The results showed that an increase in electron and ion density resulted in an increase in the efficiency of the solar cell, whereas an increase in electron and ion temperature led to a decrease in efficiency. We also studied the variation in efficiency in relation to the absorber layer of the proposed solar cell structure. The results obtained were consistent with those from previous studies based on solar cells.

Density functional theory (DFT) methods are employed to investigate the capability of B₃₆ borophene nanosheets as sensors for detecting the bromoacetone (BCT) molecule. An evaluation of the structural and electronic properties of both BCT and B₃₆ borophene is conducted. Subsequently, through computed metrics such as adsorption energy, charge density difference, and density of states, the interaction between B₃₆ and the BCT molecule is examined via dispersion-corrected density functional theory (DFT). Employing the reduced density gradient approach for the analysis of non-covalent interactions, we further explored the nature of these interactions. The obtained results illustrate that B₃₆ borophene nanosheets serve as effective sensors for the BCT molecule, showcasing their ability to adsorb up to five BCT molecules through an exothermic process. BCT molecules chemiadsorb onto B₃₆ borophene by forming B‒O covalent bonds, engaging the oxygen atom of the carbonyl group in BCT with the edge boron atoms of B₃₆ borophene. Additionally, BCT molecules physio-adsorb on both the concave and convex sides of B₃₆ borophene, facilitated by van der Waals interactions. Ab-initio molecular dynamic simulations confirm the thermal stability of the BCT@B₃₆ concave and convex complexes at both 300 K and 400 K.

The energy-loss tensor of bilayer and monolayer graphene is calculated according to the model expressed in Su et al. (Phys Rev Lett 42: 1698 1979). The size and geometry of the nanoscale carbon systems play an important role in their optical properties. Absorption bands of bilayer and monolayer graphene in the 2.81–8.0 eV region indicate sharp structures in each band. The molecular structure of these bands is localized and their crystalline order is long-range. In the x-direction of the electric field, the dielectric tensor and the energy-loss tensor of bilayer and monolayer graphene have the maximum amount. The importance of results for diamond, fullerene, graphite, and graphene is discussed.

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.