Journal of Computational Electronics最新文献

{"title":"Exciton dynamics and absorption in GeSn/SiGeSn MQWs","authors":"Alan Abdi,&nbsp;Tim Alexewicz,&nbsp;Dirk Schulz","doi":"10.1007/s10825-026-02596-3","DOIUrl":"10.1007/s10825-026-02596-3","url":null,"abstract":"<div><p>We develop a fully quantum-dynamical framework for modeling excitonic effects in group-IV semiconductor heterostructures based on a single-band (1s) tight-binding Hamiltonian. The model incorporates position-dependent band-edge potentials for electron and hole confinement, long-range Coulomb interaction, and external electric fields. Starting from the total Hamiltonian <span>(hat{H}^{tb} = hat{H}^{tb}_0 + hat{H}^{tb}_{em} + hat{H}^{tb}_C)</span>, we derive the equations of motion for the density matrix in superoperator form and solve them numerically using an adaptive Runge–Kutta scheme to ensure temporal accuracy for fast field-driven dynamics. This approach enables the calculation of stationary exciton states as well as time-resolved electronic and optical observables in realistic one-dimensional heterostructures, including single and multiple quantum wells. We systematically analyze how excitonic binding energies, absorption spectra, and carrier dynamics depend on Sn composition, quantum well geometry, and external driving fields. While multi-quantum well configurations can be engineered to modify the optical response, our results show that absorption enhancement critically depends on the spatial overlap and Coulomb coupling between confined electron and hole states. The presented framework provides microscopic insight into excitonic processes relevant for the design of Sn-based group-IV photonic materials operating in the mid-infrared regime.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10825-026-02596-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465339","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":"Symmetric physics-based compact core model for double-gate junctionless transistors with ungated extensions","authors":"Miltiadis Κ. Nakos,&nbsp;Theodoros Α. Oproglidis,&nbsp;Dimitrios Η. Tassis,&nbsp;Constantinos Τ. Angelis,&nbsp;Charalabos Α. Dimitriadis,&nbsp;Andreas Tsormpatzoglou","doi":"10.1007/s10825-026-02590-9","DOIUrl":"10.1007/s10825-026-02590-9","url":null,"abstract":"<div><p>This work presents a physics-based compact model for double-gate junctionless field-effect transistors, with emphasis on accurately capturing the impact of ungated source/drain extensions on the drain current characteristics. The model is validated against two-dimensional device simulations performed using Silvaco ATLAS for two channel doping concentrations and a wide range of ungated extension lengths. To isolate the contribution of the access regions and clarify the effective channel length, all mobility degradation models were disabled in the simulations, allowing the observed current degradation to be attributed solely to the series resistance of the ungated extensions. The proposed formulation includes an analytical factor <i>ξ</i> that accounts for the reduced electrostatic influence of the source and drain terminals on the channel potential, as well as a closed-form expression for the fringe capacitance associated with the ungated regions. The resulting drain current model demonstrates very good agreement with numerical simulations across different geometries and doping levels. Model symmetry is further verified through a Gummel symmetry test, confirming the physical consistency of the formulation. Owing to its analytical nature and physical transparency, the proposed model is well suited to serve as a core building block for higher-level compact models of JL devices.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380877","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":"Artificial neural network-assisted modeling of GAAFETs for geometry-dependent electrothermal co-simulation","authors":"Ziping Wang,&nbsp;Linhui Lai,&nbsp;Fei Li,&nbsp;Yabin Sun,&nbsp;Yanling Shi,&nbsp;Bingyi Ye,&nbsp;Yuhang Zhang,&nbsp;Yang Shen,&nbsp;Xiaojin Li","doi":"10.1007/s10825-026-02598-1","DOIUrl":"10.1007/s10825-026-02598-1","url":null,"abstract":"<div><p>Self-heating effects (SHEs) pose significant challenges for advanced semiconductor devices, particularly gate-all-around field-effect transistors (GAAFETs). Although finite element simulation and thermal network modeling serve as robust electrothermal analysis tools, their reliance on intricate physical mechanisms and numerous parameters results in prohibitively high computational costs. By contrast, machine learning (ML) techniques provide an innovative paradigm for predicting device behaviors. In this work, a methodology for developing artificial neural network (ANN) models to rapidly integrate nanosheet geometry-dependent electrothermal properties of advanced GAAFETs into device and circuit simulations is proposed. To accurately replicate SHEs-induced I–V and T–V characteristics, the ANN model is optimized through output nonlinearity control and input range management via conversion functions and a logarithmic transformation-based preprocessing scheme. The trained model achieves test accuracy of ~ 98.5% for drain current and ~ 99% for temperature prediction across diverse geometries and supply voltages. These precise predictions demonstrate that ANN-based model effectively captures underlying thermal physics, exhibiting strong capability and generality for circuit-level performance evaluation.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380876","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":"Unified parameter estimation of multi-diode photovoltaic cells and multi-cage induction machines using a hybrid simulated annealing-equilibrium optimizer","authors":"Mihailo Micev,&nbsp;Martin Ćalasan,&nbsp;Shady H. E. Abdel Aleem","doi":"10.1007/s10825-026-02584-7","DOIUrl":"10.1007/s10825-026-02584-7","url":null,"abstract":"<div><p>This paper presents a unified framework for estimating the parameters of equivalent circuits of solar photovoltaic (PV) cells and induction machines. The framework is based on a novel metaheuristic algorithm that relies on the hybridization of the Simulated Annealing (SA) and Equilibrium Optimizer (EO) algorithms, referred to as SA-EO, proposed in this work, which integrates the advantages of local exploitation and global exploration to enhance convergence and accuracy. According to the available literature, solar PV cells can be represented using single-, double- and, most accurately, triple-diode equivalent circuits. In a similar way, an induction machine (IM) can be modeled using single- and double-cage equivalent circuits. The estimation of parameters for both solar PV cells and IMs can be performed using either the manufacturer’s data or experimentally recorded measurements. In both cases, the parameters are typically extracted using analytical, numerical, or metaheuristic optimization techniques. Based on the analogy between multi-diode models of solar PV cells and multi-cage models of IMs, this paper introduces a unified parameter estimation framework applicable to all solar cell and IM configurations. The proposed hybrid metaheuristic algorithm is tested on several benchmark models reported in the literature and further validated experimentally. The obtained results confirm that the proposed unified framework offers improved accuracy, faster convergence, and greater robustness compared to existing parameter estimation methods for both solar PV cells and IMs.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380878","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":"Electrical modeling and crosstalk analysis in coupled carbon nanotube–graphene nanoribbon mixed structure interconnects","authors":"Zhican Lin,&nbsp;Zhongliang Pan,&nbsp;Weiguo Ni,&nbsp;Xinyu Li","doi":"10.1007/s10825-026-02586-5","DOIUrl":"10.1007/s10825-026-02586-5","url":null,"abstract":"<div><p>In order to reduce the crosstalk of carbon nanotube interconnects, in this paper we propose four mixed structures of carbon nanotubes and graphene nanoribbon (viz. CGMS-1, CGMS-2, CGMS-3 and CGMS-4) and establish crosstalk models for two-line and three-line structures. The electrical parameters of the proposed structure are obtained through decoupling methods, and the transfer function and bandwidth are calculated using the ABCD matrix method. Peaking inductance is also used to expand the bandwidth. The impact of crosstalk on bandwidth and delay is analyzed using two-line and three-line structures. It is found that the bandwidth of the proposed structure decreases with the increment of length, temperature and load resistance and increases with the increment of graphene thickness. The delay increases with the increment of length and temperature and decreases with the increment of graphene thickness. The changes in bandwidth and delay caused by the increase in diameter are not consistent for the four mixed structures. Compared with out-of-phase mode, the bandwidth and delay performance of mixed structures in the in-phase mode is better. In the three-line structure, the “uuu” mode has the largest bandwidth, the lowest delay and the least impact from crosstalk. Among the four mixed structures, CGMS-3 has the largest bandwidth and the smallest delay. Moreover, the bandwidth and delay performance of the mixed structures are superior to SWCNT and MWCNT and are less affected by crosstalk compared to SWCNT and MWCNT.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323758","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":"Electric field-induced band quenching and semiconductor-to-metal transition in bilayer (alpha _{12})-borophene nanoribbons","authors":"Shovon Sarkar,&nbsp;Md. Niloy Khan,&nbsp;Mahbub Alam","doi":"10.1007/s10825-026-02592-7","DOIUrl":"10.1007/s10825-026-02592-7","url":null,"abstract":"<div><p>Borophene has attracted increasing interest for nanoscale electronics because its polymorphic bonding network gives rise to unusual structural and electronic properties. In this work, we investigate the structural stability, electronic properties, and electric field-dependent transport response of bilayer <span>(alpha _{12})</span>-borophene and its zigzag nanoribbon derivatives using first-principles calculations combined with a Wannier-based quantum transport framework. Starting from the optimized monolayer <span>(alpha _{12})</span>-borophene structure, AA- and AB-stacked bilayer configurations are first examined to identify the stable parent phase for nanoribbon construction. The AB-stacked bilayer is found to be dynamically stable and semiconducting, whereas the AA-stacked configuration is excluded from further electronic and transport analysis due to its dynamical instability. Maximally localized Wannier functions are then constructed from the AB-stacked bilayer electronic structure, and the resulting Wannier Hamiltonian is validated against the direct DFT band structure before being used to generate finite-width zigzag nanoribbons. The width-dependent nanoribbon calculations show that quantum confinement modifies the electronic gap, with the bandgap increasing rapidly for narrow ribbons and approaching a nearly saturated value for wider nanoribbons. For a representative zigzag nanoribbon with a width of 5.27 nm, the perpendicular electrostatic potential drop progressively reduces the bandgap from 0.587 eV at zero field to 0.110 eV at <span>(E_z=4.14)</span> V/nm, followed by complete gap closure at <span>(E_z=4.83)</span> V/nm. The density of states confirms finite spectral weight near the Fermi level at the gap-closed field, while the transmission and conductance spectra show strong transport gap narrowing under high-field modulation. These results show that bilayer <span>(alpha _{12})</span>-borophene zigzag nanoribbons possess strong width and field-dependent electronic tunability, suggesting their relevance for high-field dual-gate and reconfigurable nanoscale switching concepts.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323429","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":"Small-signal parameter extraction of enhancement-mode MgZnO/ZnO HEMT using advanced optimization algorithms for high-frequency microwave applications","authors":"K. Vinothkumar,&nbsp;A. Kaleel Rahuman","doi":"10.1007/s10825-026-02572-x","DOIUrl":"10.1007/s10825-026-02572-x","url":null,"abstract":"<div><p>This study presents a new approach for extracting small-signal parameters in enhancement-mode MgZnO/ZnO high electron mobility transistors (HEMTs), combining a traditional analytical method with advanced optimization algorithms. The extrinsic parameters encompass parasitic capacitances, inductances, and resistances, which are extracted under pinch-off conditions. In contrast, the intrinsic parameters of the small-signal equivalent circuit (SSEC), including gate forward and backward conductance, are determined with precision. Three advanced optimization techniques, quantum genetic algorithm (QGA), covariance matrix adaptation evolution strategy (CMA-ES), and iterative-focusing tree-structured Parzen estimator (IF-TPE), are utilized to minimize the discrepancy between modeled and measured <i>S</i>-parameters. The optimized SSEC models are validated by comparing them with TCAD simulations and existing experimental benchmarks, demonstrating that the QGA-based approach attains the lowest error rate of 1.1%. The MgZnO/ZnO HEMT structure exhibits a threshold voltage of 1.15 V, a maximum extrinsic transconductance of 1520 mS/mm, a saturation current of 1.6 A/mm, a unity-gain cutoff frequency (<i>f</i>_<i>T</i>) of 540 GHz, and a maximum oscillation frequency (<i>f</i>_max) of 770 GHz, as demonstrated by DC and RF simulations. The results validate the appropriateness of the proposed device and modeling methodology for high-frequency, high-power microwave, and millimeter-wave applications.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323498","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":"Numerical simulation study on the electrical properties of tin-based perovskite TFT based on bilayer structure","authors":"Yingtao Xie,&nbsp;Baocheng Xia,&nbsp;Yuan Zhang,&nbsp;Rui Xie,&nbsp;Xin Deng","doi":"10.1007/s10825-026-02585-6","DOIUrl":"10.1007/s10825-026-02585-6","url":null,"abstract":"<div><p>The methylammonium tin iodide (MASnI<span>(_3)</span>) thin-film transistor (TFT) still has key electrical performance problems such as threshold voltage drift and unstable mobility in practical applications. This is mainly due to the unreasonable distribution of defect density of states (DOS) caused by inherent defects in the material (such as tin vacancies). To address this deficiency, this paper proposes a novel bilayer MASnI<span>(_3)</span> TFT structure, which utilizes the interlayer chemical potential difference to suppress the formation of tin vacancies, thereby optimizing the defect density distribution to improve device performance. Based on the two-dimensional device simulator of TCAD, the influence of defect DOS parameters such as tail state-donor state, Gaussian-class dependent state on the electrical characteristics of the device was systematically studied. The simulation results show that, compared with the traditional single-layer structure, the bilayer MASnI<span>(_3)</span> TFT can adjust the threshold voltage to about 1 V and stabilize the saturation mobility at 11 cm<span>(^2)</span>/V<span>(cdot)</span>s, providing a new structural optimization scheme for solving the performance bottleneck of tin-based perovskite TFT and offering theoretical guidance for the preparation of high-performance perovskite TFT.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281525","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":"Hard-constrained physics-residual neural digital twin for nonideal memtranstor emulator hysteresis prediction","authors":"Sumit Vyas","doi":"10.1007/s10825-026-02587-4","DOIUrl":"10.1007/s10825-026-02587-4","url":null,"abstract":"<div><p>Memtranstor emulators offer a circuit-level approach to accurately emulate the charge-flux hysteresis phenomenon, but prediction is challenging with the inclusion of leakage, active-block nonlinearities, saturation, and noise. In this paper, a hard-constrained physics-residual neural digital twin for nonideal memtranstor emulator hysteresis prediction is presented. A research-grade synthetic dataset was created with a nonideal circuit-based teacher model, which includes flux and memory leakage, input perturbation, saturation-limited inverse memtranstance, and nonlinear transconductance variation. The emulator cases were created using Latin Hypercube Sampling, resulting in 300 cases with a split of 210 training, 45 validation, and 45 test cases, providing 1.8 million time-domain samples. The proposed model assumes an ideal compact memtranstor equation as a physics prior, and learns a neural residual correction to inverse memtranstance, and the charge-flux relation <i>q</i>_pred = <i>φ M</i>_T,pred⁻¹ is enforced by construction. It was compared to an ideal compact physics baseline and a pure ANN baseline. The proposed model achieved a <i>R</i>² = 0.9719 and <i>R</i>² = 0.9205 for charge and inverse memtranstance prediction, respectively, and the RMSE for charge prediction was also improved by 32.60% and the RMSE for inverse memtranstance prediction was improved by 10.33% compared with the ANN baseline. These results show that hard-constrained physics-residual learning not only keeps the physically consistent hysteresis behavior but also enhances the generalization.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281518","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":"Shape-aware dissimilarity measures for process TCAD model calibration","authors":"Roman Kostal,&nbsp;Tobias Reiter,&nbsp;Stefano Di Nicola,&nbsp;Manuel Kleinbichler,&nbsp;Paul Manstetten,&nbsp;Lado Filipovic","doi":"10.1007/s10825-026-02582-9","DOIUrl":"10.1007/s10825-026-02582-9","url":null,"abstract":"<div><p>Traditional calibration of semiconductor TCAD models for topography modifying processes often relies on matching critical dimensions (CDs). This approach is prone to subjective measurement and fails to capture overall profile shape differences, thereby limiting the accuracy and effectiveness of model optimization. In this work, we address this limitation by adapting computer vision techniques for TCAD model calibration. We comprehensively evaluate several shape dissimilarity measures (SDM): the established Chamfer Distance (CHD) and two novel level-set-based measures, the Area Difference (AD) and the Sparse Field Distance (SFD). Through a rigorous computational case study, testing for sensitivity, robustness, and efficiency, we demonstrate that SFD and CHD significantly outperform traditional CD matching and area-based methods for semiconductor profile comparison derived from a representative scanning electron microscopy (SEM) image. These standardized SDMs are crucial for leveraging emerging large-scale electron microscopy datasets and enabling robust, machine learning-driven model development.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":"25 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10825-026-02582-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261820","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}