Journal of Computational Electronics最新文献

As VLSI technology scales to sub-7 nm nodes, interconnect-related delay and power dissipation become dominant design bottlenecks. This paper presents a comprehensive mathematical framework for modeling and optimizing interconnects in very-large-scale integration (VLSI) systems under delay constraints. Leveraging signal processing theory and circuit-level modeling, we introduce an enhanced delay model incorporating Elmore delay, crosstalk effects, and capacitive coupling. A constrained optimization strategy using Lagrangian relaxation and Karush–Kuhn–Tucker conditions is applied to minimize dynamic power while preserving signal integrity. Simulation results on 7 nm process technology demonstrate that the proposed method achieves up to 23% reduction in power with marginal delay overheads. Our framework is validated using HSPICE and Cadence Spectre on standard ISCAS85 and OpenCore benchmarks, providing a practical path to energy-efficient interconnect design.

We report a theoretical investigation of the designed 1,8-naphthalene imide-based dyes for application in p-type dye-sensitized solar cells (p-DSSCs). The designed dyes are metal-free organic molecules combined with a carbazole donor, a naphthalene imide acceptor, and a cyanocarboxylic acid anchoring group. Different linkers, including benzothiadiazole, phenyl, furan, and thiophene, were introduced to modify their properties. The p-DSSCs were theoretically evaluated with five various p-type semiconductors (CuO, Cu₂O, CuGaO₂, CuCrO₂, and CuAlO₂) and six various electrolytes based on cobalt and copper complexes. Computational analysis was performed by means of Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT). For all designed dye, the HOMO levels were sited below the valence band of the semiconductors, while the LUMO levels were located above the redox potential of the electrolytes. This alignment confirms hole injection and dye regeneration. The results show that the dyes, especially those with benzothiadiazole and phenyl linkers, are promising dyes for improving p-DSSC efficiency through enhanced light harvesting, effective charge separation, and reduced recombination losses. These findings contribute valuable insights into the design of high-performance p-type photosensitizers for tandem DSSC applications.

Innovative small molecule donors (SMDs) in organic solar cells (OSCs) have gained attention due to their high absorbance and tunable band gaps, enabling improved efficiency and performance. In this study, three novel SMDs (M1, M2, and M3) were proposed by modifying terminal hydrogen atoms with fluorine (M1), methyl (M2), and methoxy (M3) groups. These substitutions were systematically analyzed for their effects on structural, electronic, and optical properties using density functional theory (DFT). The HOMO–LUMO energy gaps were found to be 2.03 eV (M1), 2.02 eV (M2), and 2.00 eV (M3). M3 also exhibited the highest absorption wavelength (λ_max) of 743 nm, the lowest excitation energy (1.67 eV), and the highest light-harvesting efficiency (LHE = 0.9996). Charge transfer analyses showed that M3 had the lowest electron reorganization energy (λ_e = 0.0046 eV), indicating superior charge mobility. These findings suggest that M3 is the most promising candidate for efficient OSC applications. Computations were performed using the Gaussian 09 suite, employing the B3LYP functional with the 6-31G(d,p) basis set and TD-DFT for excited state calculations. Solvent effects were considered using the PCM model, and CAM-B3LYP was used for excitation energy validation.

Biologically inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of a Hopfield network for the function of associative memory. Spin-NeuroMem is equipped with energy-efficient spintronic synapses which utilize magnetic tunnel junctions (MTJs) to store weight matrices of multiple associative memories. The proposed synapse design achieves as low as 17.4% power consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem also encompasses a novel voltage converter with a 53.3% reduction in transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5 M(times) speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work paves the way for the development of energy-efficient and scalable neuromorphic computing systems.

We present a numerical framework for solving the Wigner–Moyal equation. While Moyal’s form is renowned for its similarity to classical dynamics, it has remained unusable for several decades due to severe numerical instability. This instability arises from the Moyal bracket not being constrained by the uncertainty principle, resulting in unbounded nonlocality. We demonstrate that excessive nonlocality can be suppressed by expanding the observation window to the uncertainty limit, rendering the problem well-posed. Our approach naturally reduces to the Boltzmann equation in regions where quantum effects are negligible; opening a new device simulation methodology that bridges classical and quantum dynamics.

Accurately modeling solar photovoltaic (PV) systems requires extracting a set of unknown parameters, it is a complex, nonlinear optimization problem involving multiple variables. This study introduces six targeted modifications to the improved whale optimization algorithm (IWOA), designed to enhance its efficiency and reliability. By incorporating a blend of algebraic and transcendental functions, these modifications improve the algorithm’s ability to balance exploration and exploitation. Performance was evaluated through ten benchmark functions, with results analyzed statistically. The enhanced algorithm was then applied to estimate nine unknown parameters in the three-diode PV model, known for its high accuracy in simulating real PV behavior. Validation against real-world data from commercial PV modules, such as the KC200GT and MSX-60, demonstrated that the modified IWOA can generate highly precise models. The results confirm the algorithm’s potential as a practical and robust tool for PV system modeling.

We investigate a one-dimensional photonic crystal (1D-PhC) structure with a central defect layer designed for optical biosensing applications, particularly for algae detection. The structure consists of alternate layers of silicon dioxide (SiO₂) and titanium dioxide (TiO₂). A defect layer, representing the biological sample, is introduced at the center generating a confined defect mode within the PBG. Using the transfer matrix method, we explore the effects of structural parameters, including the number of unit cells, defect layer thickness, and angle of incidence, on the transmission spectra to optimize the structural parameter. Finally, the biosensor’s performance is evaluated by simulating various algae species as defect layers. It is to mention that Green algae offers a peak shift to 584.1477 nm and FWHM of 0.060993 nm with QF of 9577.32. Other species show similar tunability and further cause redshifts in the resonance wavelength. Distinct shifts in the resonance wavelength confirm the sensor’s high sensitivity and selectivity demonstrating the potential of the device as a robust, label-free platform for environmental biosensing. Hence, such new idea is based on the detection of the presence of Aquatic Algae in water that creates water pollution hazardous for human and animals and plants.

In this work, we introduce a new calculation method for disordered interacting electron systems. Since both the Coulomb repulsion and impurity potential modify the system band structure and hence its electronic properties, we investigate the competition between the electrons’ Coulomb repulsion potential and the impurity potential in changing the system band structure and its phase diagram. This method is applied to a disordered interacting electron square lattice system. The advantages of our method include eliminating the influence of random numbers in the Monte Carlo process and avoiding computational errors caused by repeated evaluations of Green’s function. For comparison of the advantages of our multi-site versus single-site methods, the renormalized band structure in the dynamical mean field theory (DMFT) plus coherent potential approximation (CPA) and the multi-site beyond effective medium supercell approximation (BEMSCA) are calculated. By using realistic calculated band structures, we investigate the competition between the Coulomb interaction and impurity potential parameters in the system phase diagram. Our calculated renormalized band structures show that the ((delta = 4.0t), u = 0) point is a point at which band splitting is observed. By increasing the Coulomb repulsion, u, the energy gap between split bands reduces and completely disappears at u_c1 = 3.11t and u_c1 = 2.7t for the DMFT+CPA and four-site BEMSCA, respectively. For Coulomb repulsion strengths greater than u_c1, u > u_c1, the two bands merge into a single energy band, hence creating a paramagnetic metallic state. The metallic state occurs in a region where the strength of the Coulomb interaction is large enough to overcome the disorder potential effects. This metallic state extends until u_c2 = 13.99t and u_c2 = 8.15t for the DMFT+CPA and four sites for BEMSCA, respectively. These metallic states are sandwiched between two insulator states, band insulation u < u_c1 and Mott insulation u > u_c2. Another important result is the creation of a flat valence band at the Fermi energy for special Coulomb repulsion strengths. The flattening of the valence band can be considered as a mechanism contributing to the high-temperature superconductivity in ceramic superconductors.

Utilizing a two-band tight-binding Hamiltonian model in conjunction with Green’s function methodology, this study examines the effects of localized (sigma) and delocalized (pi) electrons on the density of states, Pauli paramagnetic susceptibility, and electronic heat capacity of a T-graphene sheet. The analysis reveals an expansion in the bandwidth and an increase in the number of Van-Hove singularities. Importantly, in addition to the magnetic characteristics, which encompass diamagnetism in graphene-based nanosystems, a paramagnetic response linked to the itinerant (pi) electrons can also manifest. Furthermore, a Schottky anomaly in the heat capacity has been observed at various temperatures, attributed to the contributions from the (sigma) and (pi) bands. This investigation underscores the significant contributions of both (sigma) and (pi) electrons to the aforementioned physical properties.

Bipolar junction transistors’ (BJTs’) dependability in radiation-hardened electronics, nuclear instrumentation, and space systems is adversely affected by total ionizing dose (TID)-induced degradation, which presents a serious obstacle to the longevity and functionality of the device. Proactive maintenance and efficient reliability assessment depend on the accurate prediction of such degradation. This research tackles this issue by creating a thorough data-driven framework that makes use of sophisticated supervised machine learning (ML) models, such as light gradient boosting machine, extreme gradient boosting, and categorical boosting (CatBoost), in addition to ensemble techniques like Stacking and Voting regressors. An 80/20 train-test split and rigorous fivefold cross-validation were used to ensure model robustness, and a carefully selected experimental dataset of 565 data points from different BJT types was used. The metaheuristic pufferfish optimization algorithm (POA) was used to systematically perform hyperparameter tuning, which significantly improved predictive performance. With a test R² of 0.9827, RMSE of 0.0926, and MAE of 0.0628, the POA-Voting model outperformed the rest of the models in terms of accuracy. The models showed accurate and dependable degradation forecasts, continuously keeping mean absolute percentage errors (MAPE) below 2.1%. Comparative studies demonstrated POA’s superior hyperparameter optimization over a genetic algorithm, while SHAP analysis validated the dominant influence of total ionizing dose on degradation. Real-time monitoring, prognostics, and improved device design in crucial radiation-exposed applications are made possible by the resulting ML pipeline, which provides an interpretable and precise tool for predicting radiation-induced transistor degradation.