Journal of Computational Electronics最新文献

Two dimensional (2D) materials carry great potential for future nanoelectronics and optoelectronics applications. These applications are very much based on the electrical properties of 2D materials. Therefore, study of the important properties of such materials is necessary for active advancement in the field. Based on this understanding, our work focuses on the study and comparison of the optical characteristics of MoTe₂, hetero bilayer 2H-type MoTe₂/MoSe_2, and MoTe₂/WSe₂ double gate MOSFET (DGMOSFET) photodetectors in the near infrared region (212-400 THz). A hybrid simulation method that uses both QuantumWise ATK and Sentaurus TCAD is used for the study. In this method, first the electrical parameters such as bandgap, effective mass, and mobility of 2H-bilayer MoTe₂ and heterobilayer MoTe₂/MoSe₂ and MoTe₂/WSe₂ are obtained using the QuantumWise ATK. Second, they are transported to Sentaurus TCAD for device simulation. To obtain accurate device characteristics, appropriate models such as the kinetic velocity model (KVM) and quantum model to account for the ballistic mobility and quantum effects in the device are used in TCAD to account for the ballistic mobility and quantum effects. The drain current characteristics, electric field, threshold voltage, electron concentration, and electron mobility are obtained for the photodetectors. Other parameters such as sensitivity, responsivity, quantum efficiency, and signal-to-noise ratio (S/N) of the photodetectors have also been estimated. The heterostructure MoTe₂ DGMOSFET offers superior performance with higher I_on/I_off ratio (67.713 × 10⁵ A/µm), sensitivity, and responsivity.

Solar cells in space are exposed to high-energy particles and ionizing radiation, which aggravate stability and exacerbate lattice defects which are detrimental to performance. A computational analysis of the radiation hardness of kesterite CZTS-based solar cells for their potential application in space is undertaken. We simulated the CZTS degradation under radiation using experimentally reported donor–acceptor defect pairs. Under proton irradiation, the deterioration pattern of CZTS current–voltage characteristics exhibited a significant decrease in short-circuit current (J_SC) and a slight reduction in open-circuit voltage (V_OC) with increasing fluence. Performance decay is observed for fluence beyond 10¹⁴ particles/cm². The simulated results provide a qualitative representation of CZTS degradation under 1 MeV proton irradiation, highlighting its remarkable radiation resistance. Simulation results substantiate the pragmatic prospects of CZTS space applicability.

In this work, we examine the transmission spectrum and band structure of a one-dimensional (1D) periodic structure composed of loops and resonators. This structure exhibits passbands separated by wide band gaps in which the propagation of electromagnetic waves is forbidden. In particular, the number of passbands and band gaps increases with the values of the system’s geometrical parameters. Furthermore, the insertion of defects at the level of the loops and resonators leads to the appearance of one or two highly localized modes within the band gaps. These modes are characterized by high transmission rates (greater than 50%) and are highly sensitive to the geometrical parameters of the structure. Their number depends on the number of introduced defects. This structure enables the design of a high-performance multi-frequency filter. The results are obtained using the Green’s Function Method (GFM) and validated through electromagnetic simulations using the Finite Element Method (FEM).

Organic photodetectors (OPDs) have attracted significant attention owing to their flexible structures, high operational capabilities, and potential for low-cost fabrication, making them promising candidates for next-generation optoelectronic applications. Herein, we investigated a novel OPD configuration comprising a PTB7-Th:PC71BM matrix blended with BPQDs with GO as hole transport layer (HTL) and PDINO as electron transport layer (ETL). The optimized OPD achieved a responsivity of 0.33 A/W coupled with a detectivity of 3.35 × 10¹² Jones. The device demonstrated a short-circuit current density (J_sc) of 19.35 mA/cm², an open-circuit voltage (V_oc) of 0.89 V, and a fill factor (FF) of 68.06%, resulting in a power conversion efficiency (PCE) of 11.75% under AM 1.5G illumination (100 mW cm⁻², 300 K). The incorporation of BPQDs into PTB7-Th:PC71BM resulted in superior charge transport capabilities and reduced recombination, which improved the device performance metrics. Machine learning-assisted modeling revealed that ensemble algorithms significantly enhance the predictive accuracy of the photodetector responsivity. Random Forest Regression achieved the highest performance, with an MSE of 0.0001362, RMSE of 0.0117, and R² of 0.9108, followed by XGBoost with an R² of 0.9028. Feature importance analysis identified the active-layer thickness (t-active) and HTL thickness (t-htl) as the most influential parameters. These findings underscore the value of combining simulations with machine learning to optimize organic photodetector design.

Accurately predicting the resonant frequencies of microstrip antennas is crucial for efficient antenna design and optimisation, yet traditional analytical and numerical methods often face challenges in handling complex parameter interactions. This paper presents a novel approach to predict the resonant frequencies of microstrip antennas using convolutional neural networks (CNNs) and image-based encoding of antenna parameters. The proposed method encodes the key design parameters—length (L), width (W), height (h), and relative permittivity (ε_r)—into 2 × 2 and 4 × 4 RGB images, where each parameter is mapped to specific colour channels or derived spatial features. These encoded images are utilized as inputs to a CNN architecture tailored for regression tasks, predicting the resonant frequency as a continuous output. The model demonstrates superior prediction accuracy for training and testing on a comprehensive dataset of microstrip antenna designs, achieving a low average percentage error (APE). The CNN effectively captures the complex relationships between antenna parameters and their corresponding resonant frequencies by leveraging spatial and feature-derived patterns in the RGB-encoded images. This approach offers a novel perspective on antenna design optimisation, enabling a highly accurate, automated, and scalable solution to predict antenna performance. The results underscore the potential of image-based encoding in enhancing the rapid design and optimisation of microstrip antennas.

The modeling of ion-sensitive field-effect transistors (ISFETs) for pH biosensing applications using a commercial Technology Computer-Aided Design (TCAD) tool has been investigated in this study. A comparative analysis was conducted to evaluate the effect of various high-k inorganic gate materials—SiO₂, Al₂O₃, Si₃N₄, ZrO₂, and Ta₂O₅—as pH-sensitive films on device performance. These materials were selected for their potential to enhance ISFET sensitivity and stability, in contrast to the conventional silicon dioxide (SiO₂) gate layer. To avoid the high cost of physical fabrication, TCAD simulation was employed as an effective and economical alternative for device development and performance evaluation. Simulation results were validated against published experimental data, confirming that ISFETs incorporating high-k materials exhibit superior sensitivity and stability compared to those using SiO₂. A key innovation in this work is the reintroduction and analysis of the site-binding electrolyte model, which is currently not available in standard TCAD platforms but is critical for accurately modeling biosensor behavior in electrolyte environments. The simulation demonstrated that Al₂O₃ and Ta₂O₅ exhibited the highest sensitivity at 59.2 mV/pH, followed closely by ZrO₂ at 59.0 mV/pH, significantly outperforming the conventional SiO₂ layer (44.6 mV/pH) across a wide pH range (approximately 1–13).

Non-fullerene organic chromophores are widely used in photovoltaic materials. In this study, the perylene-based molecules (PBI1-PBI8) with an A–π–A framework were designed by modifying the terminal acceptor of the reference compound (PBIR). Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations at the M06/6-311G(d,p) level were employed to optimize and verify their true minima structures. Further, the optimized structures were used for investigating the frontier molecular orbitals (FMOs), transition density matrix (TDM), density of states (DOS), open-circuit voltage (V_oc), and binding energy (E_b) to understand their optoelectronic and photovoltaic performances. The HOMO–LUMO energy gap of PBI1-PBI8 was obtained in a range of 2.546–2.610 eV, comparable to the PBIR reference (2.553 eV). Additionally, they showed wide absorption spectra as 571.540–599.972 nm in the gas phase and 598.871–615.031 nm in the chloroform solvent phase. The designed derivatives also exhibited lower binding energies (0.436–0.482 eV). All the new chromophores (PBI1-PBI8) showed a reasonable improvement in photovoltaic response as shown by their prominent open-circuit voltages. These results suggest that the novel perylene-based chromophores may be suitable candidates for highly efficient photovoltaic materials.

This paper presents the dimensional analysis of a micromachined vibrating beam accelerometer (VBA) for early earthquake warning system. Two beam resonators with natural frequencies of 124 kHz are determined. The primary benefit of this type of device over a capacitive accelerometer is its thickness independent sensitivity. This device serves for low-g acceleration detection with minimal noise and high sensitivity. The beams are suspended between two anchors, which support all mechanical and structural operations. The anchor dimensions have a significant impact on VBA’s natural frequency. The movement of proof mass creates an axial load on the beam when there is an external acceleration. The external acceleration application results in a shift in the frequency of vibration of beam. To see the parametric analysis, several single-beam dimensions are modeled. Proposed electromechanical and analytical mechanics of vibrating beam are used to validate the finite element method (FEM) simulation results.

With the continued scaling of integrated circuit (IC) technology nodes, optimizing interconnect performance has become critical for improving overall system performance. To overcome the limitations of single high-k dielectric insertions, we introduce a dual-dielectric approach using two distinct high-k materials within multilayer graphene nanoribbon (MLGNR) interconnects. This strategy improves carrier mobility and suppresses interfacial scattering, thereby enhancing interconnect signal transmission. The paper develops a comprehensive model that incorporates equivalent resistance, capacitance, and inductance. Based on the model, this paper applies the ABCD parameter matrix method to derive the interconnect transfer function and clarify how the dual-dielectric configuration enhances signal propagation, expands bandwidth, and reduces delay. Theoretical derivations are used to evaluate the proposed structure’s impact on key performance indicators, including mean free path (MFP), scattering resistance, delay, gain, 3 dB bandwidth, and energy-delay product (EDP). The results demonstrate that, compared to single-dielectric designs, the dual-dielectric strategy generally improves performance by reducing settling time and expanding the 3 dB bandwidth, leading to significant overall enhancements in signal transmission and efficiency. This paper provides theoretical support and data evidence for multi-dielectric design strategies in nanoscale MLGNR interconnect structures.

A memristor is a passive electrical component that connects an electric charge and the magnetic flux linkage. Due to its unique features, much research has been done on the prospects of its use in the field, including neuromorphic computing systems and memory technologies, among others. In this article, we discussed the memristive model and its modelling equations and explored the current–voltage relationships. Subsequently, we elucidated the necessity of a window function and the challenges associated with previously reported window functions. Finally, we have proposed a novel window function in this article, highlighting its advantages over numerous existing ones. The proposed window function effectively resolves the boundary lock issue, boundary effect issue, limited flexibility issue and distorted pinched hysteresis issue. Using this window function, the synaptic learning capabilities of a memristive system have also been demonstrated. The flexibility offered by this window function with just two control parameters is considerable.