Journal of Computational Electronics最新文献

In this paper, we propose a simulator and methodology for predicting the program threshold voltage (V_t) distribution in charge trap-based 3-D NAND flash memory, considering z-direction interference (Z-interference) induced by random grain boundaries (GB) within the polycrystalline silicon (poly-Si) channel. Most previous studies have modeled Z-interference by fixing the GB or have investigated the V_t distribution without including Z-interference arising from random GB characteristics. However, there is a lack of research analyzing Z-interference in the program V_t distribution that results from random GB characteristics. Consequently, electrical characteristics corresponding to cell variation and GB variation were trained into a machine learning model through Technology Computer-Aided Design (TCAD) simulations to comprehensively analyze Z-interference and Vt distribution formation in victim (Vic) cells influenced by additional factors determined by random program verify (PV) levels of aggressor (Agr) cells, thereby performing Monte Carlo simulations on random strings. The proposed simulator enables prediction of the Vt distribution with Z-interference under both random and specific GB conditions, suggesting that precise control of grain boundaries during the fabrication process can establish useful design guidelines for process optimization in 3-D NAND flash memory.

Herein we have demonstrated a comprehensive analysis of the electronic structure and transport properties of the (5, 5), (7, 7), (11, 11) CNTs encapsulated with the simple sulfur chain and helical sulfur chain. Electronic structure calculation and geometric parameter evaluation clearly depict that compared to the simple chain, the helical sulfur chain has a huge impact on the (5, 5), (11, 11) CNTs and a substantial charge transfer event occurs between these moieties. Projected density of states (PDOS) and band structure calculation confirms that both simple and helical sulfur chains couple with the conduction bands of CNTs and alter the conduction band by introduction of orbitals from the sulfur chain level. From the current–voltage curve, we can argue that up to 1 eV voltage bias, the helical sulfur chain encapsulated composite showed higher current transport and significant enhancement of quantum conductance is achieved for a (5, 5) CNT-helical sulfur composite. Hence both a simple sulfur chain and helical sulfur chain could be an active element tuning the electronic and transport properties of carbon nanotube-based nanocomposite.

Lithium-ion batteries are the power plants of our digital age, supplying energy to smartphones, laptops, electric vehicles, and energy storage systems. However, temperature significantly affects their performance. In this study, a cylindrical lithium-ion battery was simulated within an air-flow cooling chamber to investigate temperature rise and cooling performance. Key battery parameters such as voltage, current, power, and temperature were analyzed under four C-rates: 2.5, 3.5, 4.5, and 5.5. The simulation results show that the maximum temperature rise (T_Maximum) increases from 5.72 K at 2.5C to 20.36 K at 5.5C, while the average temperature rise (T_Average) and minimum temperature rise (T_Minimum) vary from 5.59 K and 5.25 K at 2.5C to 21.79 K and 20.47 K at 5.5C, respectively. Additionally, the voltage range expands with increasing C-rate, from 3.52–3.96 V at 2.5C to 3.38–4.20 V at 5.5C, and the output power reaches its peak negative value at the highest C-rate. These results quantitatively demonstrate the effect of C-rate on thermal and electrochemical performance, providing essential data for designing effective battery thermal management strategies.

This paper presents the parameter estimation of two types of light-emitting diode (LED) lamps based on experimentally recorded input current waveforms. The estimation process is formulated as an optimization problem and solved using metaheuristic algorithms. Initially, four different metaheuristics—Lyrebird optimization algorithm, Pelican optimization algorithm, Pufferfish optimization algorithm, and Red Kite optimization algorithm (ROA)—are applied to estimate the unknown parameters of the LED lamps. After identifying ROA as the most suitable algorithm, two hybrid variants are developed to further improve convergence speed and estimation accuracy. The performance of the proposed hybrid algorithms is evaluated and compared in terms of accuracy and convergence speed. Moreover, robustness analysis is conducted to assess performance under different operating conditions. The results demonstrate that the hybrid ROA variants outperform the standard algorithm, providing more precise parameter values and faster convergence for both LED lamp models. Finally, harmonic analysis confirms the accuracy of the estimation when using the proposed hybrid metaheuristic algorithms.

We present a comprehensive framework for p–n junction simulation that bridges analytical models, numerical methods, and machine learning approaches. A hybrid finite-difference physics-informed neural network solver is developed and validated across 12 silicon devices with doping concentrations ranging from (10^{15}) to (10^{19},textrm{cm}^{-3}). The surrogate model reproduces Sentaurus TCAD results with (0.48%) RMS potential error while revealing that analytical models systematically underestimate the built-in potential by up to 8 mV at high doping concentrations. This error leads to a (3%) overestimation of solar cell short-circuit current in predictive models. The framework achieves a (47times) speed-up compared to conventional TCAD simulations and is successfully extended to 2D geometries ((50 times 50) mesh) without architectural modifications, maintaining 0.62% RMS error with (12times) acceleration. All implementation code, datasets, and reproduction scripts are openly available under an MIT license.

In this work, we propose an AI-driven framework for the automated and intelligent design of photodetectors, which significantly enhances design efficiency. Specifically, by integrating a high-accuracy machine learning (ML) model with a genetic algorithm (GA) and a decision-making technique (TOPSIS), this intelligent and automated approach eliminates the need for labor-intensive manual analysis and extensive physical prototyping, while simultaneously accounting for multiple interdependent parameters. The ML model is implemented as a backpropagation-trained multilayer perceptron (BP-MLP) neural network, which effectively captures the complex and nonlinear relationships between device structure and performance, enabling rapid and accurate prediction of device characteristics across various structural configurations. Building on this, the GA can rapidly identify the optimal device structure for specific performance targets. To demonstrate its effectiveness, we design two modified uni-traveling carrier photodetectors (MUTC-PDs): a high-speed device with a 3-dB bandwidth of 246.1 GHz and a responsivity of 0.12 A/W, and a high-power device achieving a 3-dB bandwidth of 46.6 GHz and an RF output power of 31.82 dBm at 30 GHz.

Quantum rings exhibit rich physical properties due to quantum confinement and Aharonov–Bohm (AB) oscillations. While impurity-free and central-impurity rings have been widely studied, the role of anisotropic dipolar impurities remains less explored. In this work, we develop a theoretical model for a quantum ring containing a dipolar impurity under AB flux. Analytical solutions are obtained for the energy spectrum and wavefunctions, and the density-matrix formalism is employed to evaluate linear and nonlinear optical responses, including absorption, refractive index changes, and harmonic generation. Our study emphasizes the combined effect of flux periodicity and impurity-induced anisotropy as dual control parameters, highlighting the potential of dipolar impurity quantum rings for tunable nanoscale optoelectronic and photonic applications.

Negative differential resistance (NDR) devices are pivotal to the development of high-performance oscillators, where a well-defined peak-to-valley voltage difference (ΔV) enables high cut-off frequencies and ultra-low-power operation. However, engineering NDR devices with suitably low ΔV remains a persistent challenge. In this work, we present a simulation-driven design of an organic–inorganic hybrid NDR device with a multilayer heterostructure comprising ITO/Polyphenylene Vinylene (PPV)/Fullerene-C60: ZnO quantum wells/Al, modelled using SILVACO TCAD. The device exhibits distinctive multi-peak I–V characteristics, with peak-to-valley current ratios of 3.1 and 1.7, governed by donor-like trap states and resonant tunnelling transport. Remarkably, the structure achieves an ultra-low ΔV of ~ 0.05 V—an order of magnitude lower than previously reported values—highlighting its potential as a disruptive candidate for low-power, tunable, and high-frequency nanoelectronic applications.

Perovskite solar cells (PSCs) have made rapid progress in the field of clean energy harvesting supply chain due to their high-power conversion efficiency (PCE). One of the recent breakthroughs in this area is the development of fiber-based perovskite solar cells (FPSCs) with a cylindrical flexible electrode made of carbon fiber/titanium (Ti) composite and a triple-cation perovskite absorber material, Cs₀.₀₅(FA₀.₈₅MA₀.₁₅)₀.₉₅Pb(I₀.₈₅Br₀.₁₅)₃ which has better stability, efficiency, and crystalized uniform film. This study uses a bottom-up modeling approach to simulate the performance of carbon fiber-based perovskite solar cells (CFPSCs) by implementing of a classical drift–diffusion model including Fermi–Dirac statistics and Helmholtz equation in a cylindrical coordinate system. In order to get high accuracy, finite volume method (FVM) is utilized with a shape function based on centered difference scheme and Scharfetter–Gummel approximation consequently. The current evaluation framework creates a roadmap to study charge carrier dynamics, recombination mechanisms, and charge transport phenomena of cylindrical architecture. Optimization results indicate that fiber radius enhancement can improve the short-circuit current. Also, electrode potential barrier decrement may increase the open-circuit voltage. Further, the reduction of radiative recombination process coefficient, the capture cross section, total defect density, and thermal velocity of Shockley–Read–Hall recombination process and the Auger recombination process coefficient raise the fill factor. The simulation yields a short-circuit current (J_SC) of 15.344 mA/cm², open-circuit voltage (V_oc) of 1.270 V, fill factor (FF) of 79.579%, and power conversion efficiency (PCE) of 15.512%. These crucial outcomes accentuate the fiber-based perovskite solar cells application potential in future optoelectronic technology.

Graphical Abstract

Hydrogen sensors utilizing field-effect transistors (FETs) have been extensively researched in the past few decades. Silicon-based H₂ gas sensors have shown excellent performances. The next generation sensing and computing technologies demand scaling of semiconductor devices for high-density integration and inclusive performance enhancement. However, the dangling bonds and high surface scattering of silicon have restricted its application in an ultra-scaled domain. Thus, in this article, we propose an electrically doped MoTe₂-based H₂ gas sensor. We have used an analytical model to capture variation of work function with gas pressure. Next, technology computer-aided design (TCAD) tools are adopted to investigate the device performance. To understand the quantum transport in sub-10 nm MoTe₂ channel, non-equilibrium green’s function (NEGF) method is deployed. The study exhibits the high potentiality of electrically doped 2D material like MoTe₂-based H₂ sensors which may spur future experiments.