Journal of Computational Electronics最新文献

{"title":"Performance comparison between current-mode signaling and voltage-mode signaling for multilayer graphene nanoribbon (MLGNR) interconnects","authors":"Fa Zou,&nbsp;Zhongliang Pan,&nbsp;Peng Xu","doi":"10.1007/s10825-024-02274-2","DOIUrl":"10.1007/s10825-024-02274-2","url":null,"abstract":"<div><p>Graphene nanoribbon (GNR) is emerging as a superior material for nanometer-scale interconnects, offering superior performance compared with traditional copper materials. To date, most research on GNR interconnects has focused on voltage-mode signaling (VMS) scheme, with little study on current-mode signaling (CMS) scheme. In this paper, we propose an equivalent circuit model of two-wire coupled multilayer graphene nanoribbon (MLGNR) interconnects using VMS and CMS schemes. Moreover, the model takes into account influence of temperature effect, coupling capacitive and mutual inductive. Performance of victim wire in two-wire coupled MLGNR and Copper (Cu) interconnects using VMS and CMS signaling schemes is investigated by applying the decoupling approach and ABCD parameter matrix method at local, intermediate, and global levels, respectively. In addition, the performance of MLGNR and Cu interconnects employing VMS and CMS systems is thoroughly compared and examined in this research. The results reveal that interconnects adopting the CMS scheme have less output voltage swing, less crosstalk delay, greater 3-dB bandwidth, and better signal integrity, compared to interconnects applying the VMS scheme, under the same conditions. With respect to noise, we observe that the CMS scheme has lower noise amplitude, smaller noise peak, and smaller noise width, resulting in greater noise immunity. Moreover, it is manifested that crosstalk delay, noise width, and 3 dB bandwidth are all temperature-dependent. As the temperature rises, both the delay and noise width increase, while the bandwidth decreases. In addition, the results indicate that MLGNR interconnects exhibit lower crosstalk delay, narrower noise width, larger bandwidth, and smaller dynamic power consumption compared to Cu interconnects under the same conditions. Furthermore, we discuss performance optimization methods for interconnects using both VMS and CMS schemes. Also, it is discovered that there is great agreement between the results of HSPICE simulations and those produced by the ABCD parameter matrix technique.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monolayer blue phosphorene's potential for nucleobase detection: a computational study","authors":"Fatemeh Safari,&nbsp;Mahdi Moradinasab,&nbsp;Seyed-Mohammad Tabatabaei","doi":"10.1007/s10825-024-02261-7","DOIUrl":"10.1007/s10825-024-02261-7","url":null,"abstract":"<div><p>Adsorption of four canonical, two methylated, and one mutated nucleobases have been studied on single-layer blue phosphorene (SL-BlueP), including van der Waals interactions within density functional theory. Our calculations for electronic charge transfer demonstrate that all the considered bases undergo physisorption on SL-BlueP with a charge transfer within the range of -0.004 to + 0.024 |<i>e</i>|. The work function of SL-BlueP decreases by 0.08, 0.10, and 0.19 upon adsorption of adenine, cytosine, and guanine, respectively, and its bandgap can be shrunk by as much as 36%. Interestingly, the current–voltage (I-V) curves show characteristic responses depending on the type of nucleobases. Furthermore, the adsorption of nucleobase molecules on SL-BlueP gives rise to distinct energy loss spectra. The obtained distinguishable features may be used for ultraselective detection of DNA nucleobases.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly efficient XCoSi (X=V, Nb, Ta) compounds for thermoelectricity: a density functional theory approach","authors":"O. R. Jolayemi,&nbsp;G. M. Mule,&nbsp;O. T. Uto,&nbsp;O. C. Olawole","doi":"10.1007/s10825-024-02273-3","DOIUrl":"10.1007/s10825-024-02273-3","url":null,"abstract":"<div><p>Half-Heusler compounds hold great promise for thermoelectricity due to their excellent thermal stability and electronic transport properties. This study unveils the physical characteristics of half-Heusler compounds XCoSi (X = V, Nb, Ta) as potential materials for thermoelectric using the Quantum ESPRESSO and PHONOPY codes with PBEsol-GGA correlation functional. The electronic band structure calculations revealed the semiconducting nature of the compounds with an indirect band gap (X <span>(rightarrow )</span> W) of size 0.55 eV, 0.84 eV, and 1.25 eV for VCoSi, NbCoSi, and TaCoSi, respectively. The XCoSi(X=V, Nb, Ta) compounds demonstrate dynamic and mechanical stability, with ionic bonds and predicted ductility of these alloys. Additionally, critical parameters for thermoelectric application are computed, including the Seebeck coefficient (<i>S</i>), electrical conductivity (<span>(sigma )</span>), thermal conductivity (<span>(kappa )</span>), and the figure of merit (ZT). At room temperature, both p-type and n-type XCoSi (X = V, Nb, Ta) exhibit figure of merit values close to unity: 0.96 for VCoSi, 0.98 for NbCoSi, and 0.99 for TaCoSi, based solely on the electronic contribution to thermal conductivity. Including the lattice thermal conductivity provides a more accurate assessment of the thermoelectric potential of XCoSi (X = V, Nb, Ta). Among them, VCoSi shows greater potential for thermoelectric applications compared to TaCoSi and NbCoSi.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network implementation for smart medical systems with double-gate MOSFET","authors":"Epiphany Jebamalar Leavline,&nbsp;Krishnasamy Vijayakanth","doi":"10.1007/s10825-024-02246-6","DOIUrl":"10.1007/s10825-024-02246-6","url":null,"abstract":"<div><p>The implementation of a neural network on very large-scale integrated (VLSI) circuits provides flexibility in programmable systems. However, conventional field-programmable gate array (FPGA) neural chips suffer from longer computation times, higher costs, and greater energy consumption. On the other hand, multilayer perceptron (MLP) network implementation over VLSI exhibits increased speed with a smaller chip size and reduced cost. This work aims to implement an MLP neural network using double-gate metal oxide semiconductor field effect transistors (DGMOSFETs) functioning as neurons. The suggested network architecture is offered as a package utilizing very high-speed integrated circuit hardware description language (VHDL). The weights of the MLP are obtained by training a neural network with electrocardiogram (ECG) signals taken from the PhysioNet database. The ECG input signals, obtained weights and bias, are given to the designed MLP for testing. The classification accuracy of this trained neural network is 94.48%. A power analysis is also conducted for the hardware-designed MLP to validate the power reduction performance. In terms of speed, the required number of components and power, the performance of this design employing DGMOSFET outperforms its single-gate MOSFET (SGMOSFET) counterpart.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the effect of structural modifications on the performance of transistors based on black phosphorene nanoribbons","authors":"Akbar Shabani,&nbsp;Hossein Karamitaheri","doi":"10.1007/s10825-024-02268-0","DOIUrl":"10.1007/s10825-024-02268-0","url":null,"abstract":"<div><p>The modern electronic devices’ development heavily relies on the miniaturization of MOSFET transistors. On the other hand, reduction in transistor sizes will face significant challenges, like short-channel effects. To enhance transistor performance, it is essential to explore and utilize new materials. Black phosphorene has emerged as a promising material for constructing transistors and other electronic components. Accurate modeling is crucial for predicting the behavior of future nanoscale transistors. One of proposed simulation methods is the top-of-barrier model. This study analyzes transistors based on black phosphorene nanoribbons. The electronic structure of these nanoribbons is calculated using the tight-binding method with up to five nearest neighbors. The top-of-barrier computational approach within the Landauer framework is employed to determine device characteristics. Initial evaluations of a structure without antidots show that creating an off-center antidot increases the on current to 4.98 mA. The threshold voltage also rises by 0.2 V, indicating an increase in the energy band gap, which reduces the off current significantly. The on/off current ratio can be improved by up to 2500 times with an optimal antidot design. Non-central antidots do not significantly affect the threshold voltage or off current.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurobiological transition of magnetized and demagnetized dynamism for fractional Hindmarsh–Rose neuron model via fractal numerical simulations","authors":"Kashif Ali Abro,&nbsp;Imran Qasim Memon,&nbsp;Khidir Shaib Mohamed,&nbsp;Khaled Aldwoah","doi":"10.1007/s10825-024-02243-9","DOIUrl":"10.1007/s10825-024-02243-9","url":null,"abstract":"<div><p>This manuscript investigates how magnetic and non-magnetic effects influence the firing patterns, oscillations, and synchronization properties of the Hindmarsh–Rose neuron model under different magnetic conditions. The development of a fractal–fractional Hindmarsh–Rose neuron model is proposed for investigating self-similarity across different scales to analyze and understand the complexities when extreme magnetic flux varies and reaches its critical value. The mathematical modeling of the Hindmarsh–Rose neuron model is established under an application of the Caputo–Fabrizio and Atangana–Baleanu fractional differential operators. For the sake of numerical simulations via the Adams–Bashforth–Moulton method, the discretization of spatial and time domains on fractal–fractional derivatives is employed to generate numerically powerful schemes within approximate accuracy. For understanding the brain function and neural oscillations, the magnetized and demagnetized Hindmarsh–Rose neuron model revealed suppressed neuronal activity and the effects of transcranial magnetic stimulation. Our results suggested two aspects: one is trapping of neurons, striking phenomena and firing patterns under demagnetization, while the other is neurological disorders, spiking and bursting in neurons based on neural interfaces under demagnetization.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of side contact depth on the Schottky barrier of 2D field-effect transistors","authors":"L. Panarella,&nbsp;Q. Smets,&nbsp;D. Verreck,&nbsp;B. Kaczer,&nbsp;S. Tyaginov,&nbsp;C. Lockhart de la Rosa,&nbsp;G. S. Kar,&nbsp;V. Afanas’ev","doi":"10.1007/s10825-024-02262-6","DOIUrl":"10.1007/s10825-024-02262-6","url":null,"abstract":"<div><p>The performance of 2D material-based field-effect transistors (2D FETs) is significantly influenced by the vertical extension, or depth, of electrostatically doped side Schottky contacts, which is determined through etching. This study employs TCAD modeling to compare back-gated FETs with varying source/drain contact depths and channel lengths. Results indicate that deeper side contacts hinder electric field crowding at the metal/channel interface, resulting in wider Schottky barriers, diminished carrier tunneling, and reduced on-state current. In contrast, introducing a low-k dielectric beneath the source and drain yields the opposite effect. Therefore, in the development of industry-compatible 2D FETs, the depth and design of side contacts must be carefully optimized, as they are critical factors in achieving low-contact resistance devices.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10825-024-02262-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of geometrical parameters on AlGaN/GaN heterostructure MOS-HEMT biosensor","authors":"Abdellah Bouguenna,&nbsp;Driss Bouguenna,&nbsp;Amine Boudghene Stambouli,&nbsp;Aasif Mohammad Bhat","doi":"10.1007/s10825-024-02247-5","DOIUrl":"10.1007/s10825-024-02247-5","url":null,"abstract":"<div><p>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 <i>C</i>–<i>V</i> 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 <i>W</i>_g = 100 µm, <i>L</i>_g= 0.3 µm, <i>d</i>_AlGaN=15 nm, and SiO₂ oxide thickness of 25 nm at protein permittivity of 2.5.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling the resonant frequency of H-shaped microstrip antennas using a deep learning approach","authors":"Akram Bediaf,&nbsp;Sami Bedra,&nbsp;Djemai Arar,&nbsp;Mohamed Bedra","doi":"10.1007/s10825-024-02270-6","DOIUrl":"10.1007/s10825-024-02270-6","url":null,"abstract":"<div><p>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 (<i>R</i>²), 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.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shallow donor impurity states in wurtzite InGaN/GaN coupled quantum wells under built-in electric field, hydrostatic pressure, and strain effects","authors":"Guang-Xin Wang,&nbsp;Xiu-Zhi Duan","doi":"10.1007/s10825-024-02238-6","DOIUrl":"10.1007/s10825-024-02238-6","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}