Journal of Computational Electronics最新文献

In this work, we present the study of AlGaN/GaN metal–oxide–semiconductor high-electron-mobility transistor (MOS-HEMT) biosensors for protein detection. We study the effects of technological parameters including the gate width, gate length, AlGaN layer thickness, oxide thickness layer, and oxide type including HfO₂, Al₂O₃, and SiO₂ on the output characteristics, sensitivity of the MOS-HEMT biosensors, and C–V characteristics. The model developed is compared with experimental data to verify its validity. The AlGaN/GaN bio-MOS-HEMTs show the greatest change in drain current of 208.08 mA with W_g = 100 µm, L_g= 0.3 µm, d_AlGaN=15 nm, and SiO₂ oxide thickness of 25 nm at protein permittivity of 2.5.

This paper introduces a novel physics-informed learning approach that combines principles from physics with deep learning techniques to optimize the simulation process of microstrip antennas. These deep learning-based approaches are preferable because traditional full-wave models used in antenna design are computationally intensive and require significant memory due to their reliance on iterative algorithms, leading to exponential increases in resource demands as input parameters grow. In contrast, the proposed deep learning method requires significant computational resources only during training, with a constant time complexity of O(1) during deployment. This results in much faster modeling, allowing a broader range of antenna configurations to be processed more quickly, thereby improving the efficiency of the design workflow. Unlike conventional deep learning methods that rely solely on data, our approach leverages the underlying physical laws governing antenna behavior, particularly beneficial when labeled data is scarce or difficult to obtain. We propose a bias observational physics-informed learning technique by integrating physical laws into the loss function, which includes two components: Neuron Loss, the standard MSE measuring prediction accuracy against actual data, and Physics Loss, which penalizes deviations from physical laws as represented by a cavity model. The total loss combines these two, with higher physics loss indicating poorer alignment with physical principles and lower physics loss suggesting better adherence to them. This approach refines predictions by balancing data fidelity with physical constraint, wherein the dataset is sourced from simulations and real-world measurements. This strategy ensures model uncertainty and broad generalization capabilities. Computational efficiency is a key consideration, with our approach implemented on low-specification hardware, emphasizing optimal resource and power consumption. The H-shaped microstrip antennas (HMAs), known for its wide and dual-band properties, serves as the target antenna for our study. We employ three sequential models’ recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—integrated with a cavity model-driven resonance frequency representation to maintain the resonance mode TM¹⁰ at prediction. Comparative analysis of these models encompasses execution time, prediction convergence, loss reduction, prediction score (R²), as well as memory and CPU usage. This research contributes four main sections elucidating the methodology, experimental setup, and results analysis, underscoring the efficacy of our deep learning approach in antenna optimization.

In this paper, we investigated theoretically the hydrogenic donor impurity states in strained wurtzite (In,Ga)N-GaN coupled quantum wells (CQWs). The variational approach is employed to obtain the dependence on built-in electric field (BEF), hydrostatic pressure, indium composition, and structure size of the binding energy of hydrogenic donor impurity (BEHDI). The results reveal that hydrostatic pressure and structure size of the CQWs have a great influence on BEF which affects strongly the BEHDI. With the increment in hydrostatic pressure, the BEF strength of well and barrier layers enhances monotonously. However, by increasing the well width (barrier width), the BEF strength of well layer reduces (enhances) gradually, and that of barrier layers enhances (reduces). Meantime, it reveals that the binding energy (1) enhances linearly as the hydrostatic pressure is increased, (2) is more sensitive to geometrical parameters (width of well and/or barrier), and (3) demonstrates a maximum value as an impurity ion is shifted from one side of the CQWs to the other.

Perovskite solar cells (PSCs) are improving in efficiency, but their stability remains a challenge compared to other solar technologies due to the use of hybrid organic–inorganic materials. To overcome this, researchers have shifted focus from methylammonium-based PSCs to more stable cesium (Cs)-based PSCs. By optimizing multi-layer structures to enhance solar spectrum absorption, substantial performance improvements are possible. In this study, we explored single (CsPbIBr₂), dual (CsPbIBr₂/KSnI₃), and triple (CsPbIBr₂/KSnI₃/MASnBr₃) absorber layer designs. The optimization of bilayer and triple-layer PSCs takes into account various factors, such as absorber layer thickness, defect density, and interface defect density for each PSC type. Finally, using the optimal triple-absorber layer combination, we optimized the electron transport layer, hole transport layer, series resistance, and shunt resistance. In this research, we attained impressive efficiencies of 34.22% for the triple-layer solar cell, 20.41% for the bilayer solar cell, and 7.32% for the single-junction PSC. This design approach led to an optimal configuration that showed substantial improvements over the experimental benchmark, including a 7.08% increase in open circuit voltage, a 256.9% increase in short circuit current, a 22.32% increase in fill factor, and a 367.5% increase in efficiency. By meticulously aligning multiple absorber layers in perovskite solar cells, we can unlock new pathways to developing highly efficient solar cells for the future.

A photonic crystal fiber (PCF)-based multi-analyte refractive index sensor is introduced in this study for the detection of four waterborne pathogens: Vibrio cholerae, Bacillus anthracis, Escherichia coli, and Enterococcus faecalis. The sensor comprises a tri-core structure with hexagonal rings encased in a silica substrate. Two selective holes are infused with water samples, enabling concurrent detection of two analytes. The sensor integrates liquid-silica mode coupling as its sensing mechanism. The couplings are precisely estimated and numerically evaluated using a finite-element method (FEM)-based simulation tool. The optimization of the sensor’s structural characteristics resulted in wavelength sensitivity of 6386 nm/RIU, 7104 nm/RIU, 8510 nm/RIU, and 3409 nm/RIU for sample pairs of V. cholerae–pure water, V. cholerae–V. cholerae, V. cholerae–B. anthracis, and E. coli–V. cholerae, respectively. Furthermore, the sensor exhibits the highest wavelength resolution of (text {1.59} times text {10}^{-5}) RIU and figure of merit of 142 (text {RIU}^{-1}) and is also assessed for detection limit, detection accuracy, and signal-to-noise ratio. Featuring a straightforward design and remarkable sensing capabilities, the proposed sensor is anticipated to be exceptionally effective at detecting waterborne pathogens, with potential to excel in identifying chemicals, biomedical substances, and other diverse analytes.

Formulations to determine the electric field and the electrostatic potentials in a thermoelectric couple through solving the Poisson’s equation are introduced in this work. Analytical approximations of the auxiliary energies introduced in the author’s earlier work in the relaxation time approximation of the Boltzmann transport equation are developed based on the coupled equations of heat and electric current. These auxiliary energies are used in the Poisson’s equation at each temperature node along the thermoelectric leg to obtain a set of algebraic equations with the electric field and the electrostatic potentials as unknowns. The algebraic equations are then solved using the derived algorithm and the boundary conditions determined by the continuity and the carrier concentration equations.

In response to address the constraints of fullerene analogues, scientists are constantly working on developing low-cost fullerene-free functionalization for nanoscale organic photovoltaics. During the present study, the computational design and analysis of 14 new non-fullerene dyes (IDIC-O-1 to IDIC-O-14) centered on indacenodithiophene (IDIC) core are proposed with sp²-hybridized nitrogen at varying positions. Regarding their UV–visible assessment, several long-range and range-separated functionals like B3LYP, CAM-B3LYP, ωB97XD, and APFD using the 6-311G + (d,p) basis set have been employed to identify their optimal level of density functional theory (DFT) with an impressive correlation at the CAM-B3LYP level. Their global hardness (η) and global electrophilicity (ω) natures show their persistent nature. The energy gaps (E_gaps) are lesser than IDIC and IDIC-O to imply an easier electronic transition. When contrasted to the IDIC-O, the findings indicate that its broad absorption spectrum had a redshift. The efficient HOMO → LUMO-based CT was investigated, and an open-circuit voltage (V_oc) study is done on HOMO_IDIC → LUMO_Acceptor. All dyes have their V_oc values lower than reference (IDIC-O) except IDIC-O-11 with a positive value. These lower reorganization energies (R_E) for holes and electrons indicate a greater charge transfer (CT). When contrasted to the IDIC-O, the newly designed dyes have better characteristics for solar cell performance.

Graphical abstract

Electronic and thermoelectric properties of Irida-Graphene nanoribbons (IGNRs) are significantly influenced by their edge configurations. This article presents a comprehensive computational study of the band structure, density of states (DOS), transmission function, and current–voltage (I-V) characteristics of zigzag and armchair edge IGNRs. Zigzag edge IGNRs (ZIGNRs) exhibit localized edge states, which introduce a Dirac point at the Fermi level, contributing to metallic behavior and enhancing the Seebeck coefficient. In contrast, armchair edge IGNRs (AIGNRs) show semiconducting behavior with a bandgap of approximately 2.4 eV. The thermoelectric performance of ZIGNRs is superior, with a higher Seebeck coefficient and electronic figure of merit (ZT_e) compared to AIGNRs. The maximum Seebeck coefficient for ZIGNRs is about 7 μV/K, while for AIGNRs, it is about 1.5 μV/K. The ZT_e for ZIGNRs is approximately 0.007, and for AIGNRs, it is about 0.005. These findings provide valuable insights into the design and optimization of IGNRs for advanced thermoelectric and electronic applications.

The optical properties of two-dimensional (2D) Janus MoSTe photodetectors under irradiation of polarized light have attracted tremendous attention recently due to their potential applications in low power consumption nanoelectronics and optoelectronics. By using the nonequilibrium Green's function method combined with density functional theory, we theoretically investigate the optical properties of various substitution-doped Janus MoSTe photodetectors. It has been demonstrated that the photocurrents along the armchair direction for all built devices exhibit a cosine-function-like behavior, and those along the zigzag direction present a sine-function-like relationship with the polarization angle θ under irradiation of linearly polarized light. The maximum photocurrents are in range from 3.06 ({text{a}}_{0}^{2}/text{photon}) to 16.82 ({text{a}}_{0}^{2}/text{photon}) among As substituted Mo, W substituted Mo, S substituted Te, Te substituted S, and Se substituted S of the Janus MoSTe photodetectors, apparently larger than the photocurrent of 0.61 ({text{a}}_{0}^{2}/text{photon}) for pure MoSTe photodetector, since the atomistic doping significantly reduce the structural symmetry of the photodetectors. Interestingly, a maximum extinction ratio of 4.26 × 10² has been observed in Janus MoSTe photodetectors with W substituted by Mo atom, implying the ultrahigh polarization sensitivity of the Janus MoSTe photodetectors. In addition, an obvious anisotropy between the armchair and zigzag directions of system has been observed, since the generated photocurrent along the armchair direction is much larger than that along the zigzag direction. Therefore, the 2D Janus MoSTe monolayer should be a good candidate material for future nanoelectronic and optoelectronic applications.

The shallow trench isolation-based laterally diffused metal–oxide–semiconductor (STI LDMOS) is a crucial device for power integrated circuits. In this article, a novel framework that integrates an optimal objective function, Bayesian optimization (BO) algorithm, and deep neural network (DNN) model is proposed to fully realize the automatic and optimal design of STI LDMOS devices. On the one hand, given the structure of the device, the DNN model in the proposed method can provide ultra-fast and highly accurate performance estimation including breakdown voltage (BV) and specific on-resistance (R_onsp). The experimental results demonstrate 98.68% average prediction accuracy for both BV and R_onsp, higher than that for other machine learning (ML) algorithms. On the other hand, to target the specified value of BV and R_onsp, the proposed framework can fully automatically and optimally design the precise device structure that simultaneously achieves the target performance with the optimal figure of merit (FOM) of the device. Compared to technology computer-aided design (TCAD), there is only a 0.002% error in FOM and a 2.83% average error in BV and R_onsp. Moreover, considering the training time of the DNN model, the proposed framework is 100 times as efficient as other conventional frameworks. Thus, this research provides the experimental groundwork for constructing an automatic design framework for an LDMOS device and opens new opportunities for accelerating the development of LDMOS technology in the future.