Search Results (363)

Surge reliability is a crucial aspect of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) reliability. This study investigates the degradation behavior and mechanisms of switching characteristics in 1.2 kV planar-gate SiC MOSFETs under repetitive surge current. A surge current test platform is established to conduct surge tests on the device, while monitoring the evolution of its switching characteristics. The results indicate that after 4000 surge current cycles, the device’s turn-on delay time (t_d(on)), rise time (t_r), and turn-on loss (E_ON) show no significant changes. In contrast, the turn-off delay time (t_d(off)), fall time (t_f), and turn-off loss (E_OFF) increase by 9%, 7.5%, and 8.3%, respectively. Switching characteristics variations are closely linked to the reduction in threshold voltage (V_TH) and the increase in gate-source capacitance (C_GS) and gate-drain capacitance (C_GD). The degradation of these parameters stems from the accumulation of positive trapped charge in the gate oxide layer above the channel and junction field-effect transistor (JFET) region. The increase in charges results from the combined effects of negative gate bias and cyclic high temperature induced by repetitive surge current. This study provides a theoretical basis for the comprehensive understanding of the impact of surge current on SiC MOSFET performance. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

This paper presents a rule-based LVS-driven methodology for parasitic RC extraction from CMOS layouts for post-layout SPICE simulation. The proposed approach operates directly within foundry-qualified rule environments, ensuring consistency with Process Design Kits (PDKs) and enabling seamless integration with existing design and verification flows without requiring field-solver execution during the production extraction flow. The methodology provides a generalized framework for deriving electrical parameters from layout geometries and is applicable to interconnects, contacts, vias, and gate structures in multilayer CMOS technologies. By decomposing conductive regions into directional components and applying geometric and Boolean operations, the method captures the impact of layout topology and process-dependent features on circuit-level behavior. In addition, a model-order reduction technique based on π-equivalent representations is introduced to simplify the resulting networks while preserving timing accuracy. This enables the scalable simulation of complex layouts with reduced computational overhead. The proposed framework supports layout optimization, variability-aware design, and process-technology co-design, particularly for mature and advanced planar nodes. The methodology is evaluated using register-file layout test cases and post-layout SPICE simulations. The results show that the proposed rule-based extraction and RC-merging flow preserve timing behavior while reducing netlist complexity. Full article

A Controllable Line-Commutated Converter (CLCC) is a novel piece of equipment for enhancing the commutation failure resistance of High-Voltage Direct Current (HVDC) transmission systems. Traditional lumped parameter models ignore the high-frequency coupling effects of internal distributed stray capacitances, resulting in insufficient transient simulation accuracy and restricting refined engineering design. Taking the CLCC in the HVDC transformation project as the research object, this paper analyzes the distribution characteristics of stray parameters in a press-pack Insulated Gate Bipolar Transistor (IGBT) under stacked structures. By integrating distributed stray parameter networks with the nonlinear characteristics of the devices, an improved IGBT equivalent circuit model is established, with key parameters identified based on field-measured data. Furthermore, an LCC-CLCC simulation model is built and used to replace the improved IGBT model to conduct short-circuit fault simulation verification. The results demonstrate that the high-fidelity model accurately reproduces transient waveforms under Alternating Current (AC) voltage disturbance and faithfully reflects the actual operating characteristics of a surge arrester and IGBT, thereby effectively compensating for the idealized errors inherent in traditional models. This modeling methodology provides a robust theoretical and simulation foundation for parameter optimization, valve control system design, and the secure operation of a CLCC. Full article

Molybdenum disulfide (MoS₂) is a compelling candidate for visible-light detection due to its strong optical absorption and tunable bandgap, yet the development of high-performance MoS₂ photodetectors remains limited by challenges in scalable integration, low-voltage operation, and efficient photoresponse. Here, we report an ion-gel-assisted transfer strategy that enables the fabrication of large-area MoS₂/ion gel films that are suitable for low-power phototransistor applications. The transferred MoS₂/ion gel stack is laminated onto an indium-tin-zinc-oxide (ITZO) layer on a glass substrate to fabricate a MoS₂/ITZO heterojunction phototransistor, with the ion gel serving as an ultrathin, high-capacitance gate dielectric. The resulting phototransistor exhibits a field-effect mobility of 4.12 cm²/Vs, an on/off current ratio of 4.9 × 10⁵, and a subthreshold swing of 0.17 V/dec. Under 635, 520, and 405 nm illumination with a power density of 4.5 mW/cm², it achieves responsivities of 0.58, 1.82, and 5.56 A W⁻¹ and detectivities of 5.90 × 10⁹, 1.86 × 10¹⁰, and 5.68 × 10¹⁰ Jones, respectively. These findings demonstrate that the ion-gel-assisted transfer process offers a robust route to high-performance, low-voltage photodetection and provides a promising platform for next-generation optoelectronic technologies. Full article

Indium oxide (In₂O₃) has recently emerged as a promising semiconductor for advanced electronics due to its high electron mobility and wide bandgap. In this article, the lateral scaling characteristics of top-gate In₂O₃ thin-film transistors (TFTs) featuring a 1.5 nm thick channel and a 7 nm thick HfO₂ gate dielectric are investigated by two-dimensional device simulation. The analysis covers short-channel effects, DC characteristics, transconductance behavior, and small-signal radio frequency (RF) metrics across a gate-length (L_G) range of 20 nm to 700 nm. Simulation results identify a critical gate length near 100 nm for the transition from long-channel to short-channel behavior. For L_G ≤ 100 nm, pronounced short-channel effects emerge, featuring a significant negative V_TH shift and a drain-induced barrier lowering (DIBL) coefficient up to ~130 mV/V. A non-classical gm scaling behavior is observed, where g_{m_max} initially increases with L_G, then remains within a narrow range and eventually evolves toward the conventional long-channel trend. Further analysis of the lateral electric field distribution, field-dependent mobility, and transconductance efficiency indicates that this behavior originates from a crossover between short-channel field-assisted transport and gate-controlled channel modulation. The devices show strong RF potential, with f_T and f_max reaching 124.32 GHz and 157.64 GHz, respectively, at L_G = 20 nm. The high-mobility In₂O₃ channel leads to a less distinct f_T scaling transition from the classical 1/L²_G dependence to the short-channel 1/L_G dependence, while f_max scaling evolves through different regimes governed by capacitance-related limitations, intrinsic transport enhancement, and short-channel non-idealities. This work provides physical insight into the lateral scaling behavior of ultrathin top-gate In₂O₃ TFTs and highlights their potential for high-frequency and power-dense applications. Full article

Biosensors have emerged as a rapidly evolving area of research, offering transformative potential across biomedical diagnostics, environmental monitoring, and pharmaceutical applications. Among the diverse range of biosensing technologies, graphene-based surface plasmonic resonance (SPR) biosensors have attracted particular interest due to their exceptional sensitivity, scalability for mass production, and cost-effective fabrication processes. This study explores the operational principles and current design methodologies of graphene-based SPR biosensors, with a special emphasis on the role of electrolyte gating and its impact on sensor performance. Furthermore, the influence of graphene’s quantum capacitance is investigated as a critical parameter for improving the accuracy and reliability of performance predictions in the proposed sensor configuration. Computational analysis of sensitivity and key performance metrics was conducted. Notably, key performance metrics of the sensor improved upon incorporating quantum capacitance effects into the simulation framework. At a β₂-microglobulin concentration of 0.00118 g/L, the sensitivity increased to 174 GHz·g/L, the figure of merit reached 0.55 L/g, the quality factor was 0.01, the signal-to-noise ratio (SNR) rose to 0.008, and the detection accuracy (DA) reached 0.08 L/THz, demonstrating the significant impact of quantum capacitance on the sensor’s performance. These findings highlight the potential of quantum-electrostatic considerations to enhance the precision and efficacy of graphene-based SPR biosensors, paving the way for the development of next-generation biosensing platforms with improved analytical capabilities. Unlike conventional graphene SPR biosensors, which primarily detect refractive index changes near the graphene surface, our model explicitly considers the electrostatic effect of biomolecules on graphene’s Fermi energy. By modelling β2-microglobulin as a charged species, we compute the resulting electric double layer and incorporate quantum capacitance in series. This amplifies the charge-induced modulation of graphene’s optical conductivity, and, combined with a graphene perfect absorber design, leads to enhanced plasmonic resonance shifts. Consequently, our approach achieves higher sensitivity and more precise detection of biomolecular interactions compared to traditional simulations. Full article

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article

This paper presents a real-time digital twin (DT) of the power conversion system used in offshore wind applications. The proposed DT is exploited to identify key electrical parameters of both the permanent magnet synchronous generator (PMSG) and the three-phase boost rectifier and has been developed with a Condition Monitoring (CM)-oriented approach. A Gated Recurrent Unit (GRU) neural network is adopted as a real-time digital model (RTDM) to estimate online the PMSG phase resistance and synchronous inductance, as well as the DC-link capacitance at the rectifier output. The network is trained in MATLAB using data generated by a Typhoon HIL 606 emulator, covering both balanced and unbalanced operating conditions and a wide range of parameter variations. The trained GRU is then deployed on the control board and implemented in LabVIEW Real-Time for embedded execution. Experimental tests on a PMSG-based generating unit confirm the effectiveness of the proposed RTDM, achieving low root-mean-square and mean percentage errors in parameter estimation. The results demonstrate that the enhanced neural real-time DT is a promising tool for condition monitoring and predictive maintenance of power conversion systems in offshore wind applications. Full article

This paper provides a comprehensive analysis of active frequency doubler architectures adopted for efficient generation of millimeter-wave (mm-wave) signals. The operational principles of each topology are explained to address a thorough comparison based on essential performance metrics such as conversion gain, power efficiency, and spectral purity. The review covers several topologies from the standard push–push (PP) doubler to its power-efficient evolution, the complementary push–push (CPP) doubler. Furthermore, this paper focuses on more recent and advanced topologies, including the complementary common gate capacitive cross-coupled (CCGCCC) doubler. Finally, this work proposes and evaluates an improved version of the CCCGCC doubler, offering insights into the state of the art and future directions in mm-wave frequency multiplication. Full article

Modular multilevel converters (MMCs) for high-voltage direct current (HVDC) transmission require substantial submodule (SM) capacitance to limit capacitor voltage ripple, resulting in bulky and costly converter valves. The integrated gate-commutated thyristor (IGCT), with its higher voltage rating and lower conduction loss compared to the insulated-gate bipolar transistor (IGBT), enables a significant reduction in the number of SMs per arm, offering a pathway toward compact converter design. This paper investigates how the reduced SM count of IGCT-based MMCs affects the feasibility and benefit of operating with elevated capacitor voltage ripple to further decrease SM capacitance. An analytical framework is developed to evaluate the modulation boundary under increased ripple, explicitly accounting for the voltage ripple coupling (CVR) effect and circulating-current suppression. A ripple-tolerance coefficient κ is introduced, and its optimal value is determined by identifying the inflection point beyond which the achievable AC voltage output begins to decline. For a ±500 kV/2000 MW IGCT-MMC case study using 6.5 kV devices with 250 SMs per arm, the proposed method reduces the per-unit energy storage requirement by up to 39.4% compared with conventional-ripple operation. Simulation and prototype experimental results on a 400 V, 3 kW, 4-SM/arm test bench validate the analytical predictions and confirm the practical feasibility of the approach. Full article

Graphene-based ion-sensitive field-effect transistors can operate as biosensors by utilizing the formation of an electric double layer at the interface between the electrolyte and the graphene channel, enabling high sensitivity, scalability, and cost-effective fabrication. In this work, we focus on the working principles and current methodologies associated with these devices, making a comparative analysis of different models that describe the electric double layer in the electrolyte, referring to sodium ions (Na⁺) as a case study for the detection performance of the graphene biosensor, and taking into account the impact of graphene quantum capacitance. Our study addresses the sensitivity of graphene field-effect transistors within the framework of the Gouy–Chapman model, as well as the Stern model, computing device sensitivities of 3200 V/M and 5500 V/M, respectively. By incorporating the impact of graphene’s quantum capacitance in the calculations, increased sensitivity up to 5620 V/M was found. The present work shines light on the rationalization of graphene-based biosensors’ operation and performance. Full article

Reconfigurable logic is crucial for future adaptive computing, but is challenging to realize with conventional complementary metal-oxide-semiconductor technology due to the limited field-effect characteristics of the fundamental silicon devices. Two-dimensional materials offer a promising platform, yet enhancing their functional versatility requires novel operational mechanisms. Here, we demonstrate a single WSe₂/h-BN/graphene heterojunction capable of dynamically switching between distinct logic functions—XNOR and IMP (implication gate or “IF-THEN” gate)—simply by modulating the drain-source voltage. At a low bias of 0.3 V, the carrier distribution is governed by capacitive coupling, realizing an XNOR gate. Increasing the bias to 3 V activates Fowler–Nordheim tunneling between the graphene floating gate and the drain, enabling IMP logic operation. The interplay and voltage-induced transition between these two physical mechanisms underpin the device’s multifunctional capability. This work introduces a novel operational strategy for two-dimensional material-based reconfigurable logic, providing a pathway toward compact, adaptive hardware for post-CMOS computing. Full article

The performance and reliability of 4H-SiC Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) are largely determined by the material properties of gate dielectric films and the quality of the dielectric/SiC interface. This paper provides a systematic review of recent progress in gate dielectric engineering for 4H-SiC MOSFETs, with emphasis on SiO₂-based gate dielectrics and high-dielectric-constant (high-k) gate dielectrics. First, for conventional thermally grown SiO₂/SiC systems, the effects of interface nitridation, gate oxide doping, and surface pretreatment techniques are comprehensively discussed. The influence mechanisms of these processes on carbon-related interface defects, interface state density and field-effect mobility are analyzed, and the advances in related research are summarized. Second, the application of high-k gate dielectrics, including Al₂O₃, HfO₂, ZrO₂, and stacked dielectric structures, in SiC MOS devices is systematically reviewed. The advantages of these materials in reducing equivalent oxide thickness, increasing gate capacitance, suppressing leakage current, and improving thermal stability are highlighted. In addition, interface defects and electrical characteristics associated with different high-k gate dielectrics are comparatively evaluated. Finally, future research directions are discussed, including in situ interface engineering based on atomic layer deposition, dopant modulation, and heterogeneous gate dielectric structures. These approaches show strong potential for achieving high mobility, low loss, and high reliability in advanced 4H-SiC power MOSFETs. Full article

This work presents a comprehensive quantum transport modeling and simulation framework to evaluate parasitic effects and radio frequency (RF) performance in stacked silicon (Si) nanosheet (NS) lateral gate-all-around (LGAA) nFETs targeting the sub-2 nm technology node. Leveraging the non-equilibrium Green’s function (NEGF) method, the proposed framework integrates detailed modeling of parasitic resistances (R_para) and capacitances (C_para) to enable a holistic analysis of both intrinsic and extrinsic figures-of-merit, including transconductance (g_m), output conductance (g_d), cutoff frequency (f_T), and maximum oscillation frequency (f_max). The effects of nanosheet geometry, crystal orientations, and dual-k spacers on high-frequency performance are systematically investigated. The analysis reveals key design trade-offs, with optimized device configurations yielding f_T exceeding 400 GHz and f_max approaching 1.2 THz. These findings highlight the potential of stacked NS LGAA-nFETs for future millimeter-wave and terahertz applications, providing critical insights into parasitics management and quantum-transport-aware design strategies at advanced CMOS nodes. Full article