Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Juan P. Sanchez-Munoz, Svetlana C. Sejas-Garcia, Chudy Nwachukwu et al.

Far-end crosstalk between coupled microstrip lines with varying spacing is analyzed. The difference between the capacitive and inductive coupling coefficients is used to quantify this crosstalk, as it is directly proportional to this difference. It is demonstrated that, due to the presence of the solder mask, the far-end crosstalk does not always increase as the spacing between lines decreases, but it follows non-monotonic behavior. Considering this finding, data calculated from the coupling coefficients versus spacing is then used to determine the optimal spacing between a single-ended channel and a differential pair channel, in which the far-end crosstalk is minimized. Furthermore, experimental results show that the contrast between the PCB dielectric laminate and the solder mask causes the crosstalk to significantly differ in magnitude for microstrips with identical cross-sections, highlighting the importance of selecting the right combination of solder mask and dielectric laminate as a key design parameter for applications with stringent crosstalk noise specifications.

Somayeh Sharifi, Jalal Basiri Parsa, Robert Peter

Developing efficient and low-Pt electrocatalysts is critical for the commercialization of direct ethanol fuel cells (DEFC). Herein, a novel bimetallic iron-nickel metal-organic framework, MIL-88B(Fe2/Ni)-NH2 ((Fe2/Ni)MOF), was synthesized using 2-aminoterephthalic acid as a linking ligand. Different loadings of reduced graphene oxide (rGO, 1–8wt%) were incorporated via solvothermal synthesis to enhance structural stability and conductivity, forming 1–8wt% rGO-(Fe2/Ni)MOF composites. These hybrids serve as supports for Pt catalysts, producing Pt/[1–8wt% rGO-(Fe2/Ni)MOF] electrocatalysts. The synthesized materials were characterized using FT-IR, XRD, SEM, TEM, EDS mapping, XPS, cyclic voltammetry, chronoamperometry, electrochemical impedance spectroscopy, and direct ethanol fuel cell performance testing. Among the prepared catalysts, Pt/[5 wt% rGO-(Fe2/Ni)MOF] exhibited the highest electrocatalytic activity toward ethanol oxidation, achieving a current density of 50.37 mA cm−2 at 0.86 V. In DEFC testing at 60 °C with 3M ethanol, this catalyst delivered a power density three times higher than the Pt/CC as control catalyst, with an open-circuit voltage of 0.54 V compared to 0.35 V for Pt/CC. These results demonstrate that the designed rGO-MOF hybrid is an efficient and durable Pt support, offering significant potential for DEFC applications and sustainable energy conversion.

Zelin Ma, Jun Ge, Shusheng Pan

Abstract Reservoir computing (RC), a type of recurrent neural network, is particularly well‐suited for hardware implementation in edge computing. It is shown that RC hardware based on dynamic memristors potentially offers much lower power consumption and reduced computation times than digital electronics. However, challenges such as stochasticity and read noise in these devices can impair its performance. Furthermore, the external analog‐to‐digital (ADC) readout circuits may require substantial area and energy. In this work, it is experimentally demonstrated that a population of stochastic diffusive Ag:SiOx memristors can effectively construct a spiking reservoir computing system. This system demonstrates remarkable resilience to read noise and delivers exceptional performance across a range of computational tasks, achieving a 98% accuracy in waveform classification and a normalized root mean square error (NRMSE) of 0.154 in time‐series prediction. Further simulations reveal that a certain degree of device stochasticity actually enhances system performance. Without using ADC converters, a hybrid memristor‐CMOS spiking RC system is designed that demonstrates significantly lower power consumption compared to fully digital systems.

Antonin Sojka, Karl Rieger, Nikolay Agladze et al.

We present an amplitude modulator for high-power sub-terahertz radiation based on a laser-driven semiconductor switch capable of arbitrarily shaping pulse sequences with nanosecond time resolution. The core of the device is a photonic switch constructed from a 4 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m-thick epitaxially grown indium phosphide film bonded to a sapphire substrate. This switch is driven by an array of eight fiber-coupled 905 nm laser diodes, each delivering up to 125 W of peak power with programmable pulse durations ranging from 40 to 250 ns. By optimizing the semiconductor layer and substrate combination, we achieve a two-orders-of-magnitude reduction in the optical excitation fluence required for modulation, a key advancement that enables the use of compact and cost-effective laser diodes rather than high-power pulsed lasers traditionally required for such devices. The modulator was experimentally validated through a 240 GHz pulsed electron spin resonance experiment. The modulator also successfully operated with nearly 1 kW of sub-THz radiation without sustaining damage. Time-resolved reflectance measurements confirmed that the temporal structure of the laser pulses is faithfully imprinted onto the reflected sub-THz signal, demonstrating the system&#x2019;s capability for flexible and precise pulse shaping at high frequencies.

Plinio Bau, Thanh Hai Phung, Stephane Driussi et al.

This work presents a power transistor with monolithically integrated gate driver and auxiliary circuit in the same GaN-on-Si die. It presents the design, the characterization and validation tests in a PCB similarly to a final application for this device. The target application is for USB-C chargers and power supplies for data centers. The technology is 650 V pGaN with Schottky gate. Simulation from -40 to 150 °C are performed and also fabrication process variation analysis (SS, FF) compared to typical values (TT).

Gabriele Manganaro

This position paper extends the author’s keynote address from the 2024 IEEE European Solid-State Electronics Research Conference, offering a perspective on effective strategies for the advancement of analog and mixed-signal (AMS) integrated circuit (IC) design. It is argued that traditional methodologies, characterized by their focus on transistor-level optimization within individual sub-blocks, are insufficient for satisfying the stringent performance and power consumption demands of contemporary information and communication technologies (ICT), especially in the context of expanding AI applications. Consequently, a paradigm shift is necessary, emphasizing “full-stack” solutions that prioritize comprehensive system-level analysis and aim to minimize physical resources and reduce complexity by innovating across the established boundaries of design abstraction levels. Building on prior work, this manuscript offers a more thorough justification for the proposed full-stack analog design methodology, supported by broader evidence and more comprehensive discussion. It also identifies key considerations regarding EDA and workforce development as topics for future work.

Difa Maulana, Alif Subardono

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Anoy Saha, Mona Ghassemi

The electrification of aircraft is reshaping the foundations of aerospace design by positioning electrical systems at the center of propulsion, control, and onboard functionality. This chapter provides an overview of electrical system architectures for electric and hybrid electric aircraft, highlighting both established principles and emerging design strategies. The discussion begins with the motivations for electrification, including reducing environmental impact, improving operational efficiency, and replacing complex pneumatic and hydraulic subsystems with lighter and more reliable electrical alternatives. Aircraft electrical architectures are classified into four major categories: conventional, more electric, all electric, and hybrid electric. A range of system topologies is examined, including direct current (DC), alternating current (AC), hybrid, and distributed configurations. Each is considered in terms of its effectiveness in delivering power, enabling redundancy, supporting fault isolation, and managing thermal performance. Real world examples are presented to demonstrate practical applications, with case studies drawn from the Boeing 787 Dreamliner, the Eviation Alice commuter aircraft, and NASA X57 Maxwell demonstrator. These examples illustrate the ongoing transition from incremental subsystem electrification toward fully integrated architectures that promise higher efficiency and greater sustainability.

Alexander Kolesov, Stepan Manukhov, Vladimir V. Palyulin et al.

We propose $\textbf{E}$lectric $\textbf{C}$urrent $\textbf{D}$iscrete $\textbf{D}$ata $\textbf{G}$eneration (ECD$^{2}$G), a pioneering method for data generation in discrete settings that is grounded in electrical engineering theory. Our approach draws an analogy between electric current flow in a circuit and the transfer of probability mass between data distributions. We interpret samples from the source distribution as current input nodes of a circuit and samples from the target distribution as current output nodes. A neural network is then used to learn the electric currents to represent the probability flow in the circuit. To map the source distribution to the target, we sample from the source and transport these samples along the circuit pathways according to the learned currents. This process provably guarantees transfer between data distributions. We present proof-of-concept experiments to illustrate our ECD$^{2}$G method.

Forrest Mozer, Oleksiy Agapitov

The operating principles of a DC and low frequency electric field detector are developed, after which, examples of earlier important electric field measurements are presented, including, the first observation of parallel electric fields in the auroral acceleration region, the first observation of time domain structures in space, the first experimental verification of symmetric magnetic field reconnection, the first observations of triggered ion acoustic waves, and oblique whistlers that directly accelerate electrons. Future possible improvements in the electric field measurement technique are described.

Mahin Mohammadi, Saman Jamshidi, Alireza Rezvanian et al.

Accurate time series forecasting has become increasingly important across various domains such as finance, energy, and medicine. This study introduces an innovative hybrid model that leverages the power of neural networks, precisely Many To Many LSTM (MTM LSTM) and Multilayer Perceptron (MLP), to improve time series forecasting accuracy. In this new combination, we trained network MTM LSTM to approximate the target at each step, and finally, we used network MLP to combine these approximations. To perform the evaluation, we made a forecast for the amount of solar energy radiation in the city of Mashhad, Iran. The experiment results concerning MSE and MAE showed that the proposed method with five lags outperforms the standard models.We hypothesize that MTM LSTM can effectively capture solar radiation's intricate temporal dependencies and nonlinearity. At the same time, MLP can enhance function approximation by modeling complex interactions, resulting in improved forecast accuracy. By employing the hybrid MTM LSTM and MLP model, we achieved improved accuracy in predicting solar energy radiation, which has significant implications for the renewable energy sector and its energy management and planning applications. This research advances time series forecasting techniques, highlighting the effectiveness of combining neural networks to address complex and dynamic patterns in time-dependent data.Overall, our findings underscore the potential and efficacy of the proposed hybrid model as a robust tool for accurate time series forecasting in various domains, supporting effective decision-making and planning processes.

Pavel A. Andreev, Mariya Iv. Trukhanova

The spin current model of electric polarization of multiferroics is justified via the quantum hydrodynamic method and the mean-field part of the spin-orbit interaction. The spin current model is applied to derive the electric polarization proportional to the scalar product of the spins of the near by ions, which appears to be caused by the Dzylaoshinskii-Moriya interaction. Symmetric tensor spin structure of the polarization is discussed as well. We start our derivations for the ferromagnetic multiferroic materials and present the further generalization for the antiferromagnetic multiferroic materials. We rederive the operator of the electric dipole moment, which provides the macroscopic polarization obtained via the spin current model. Finally, we use the quantum average of the found electric dipole moment operator in order to derive the polarization evolution equation for the antiferromagnetic multiferroic materials. Possibility of spiral spin structures is analyzed.

Chi-Yu Lai, Yin-Song Liao, Jyh-Pin Chou et al.

The rapid development of portable electronics and emerging electric vehicles has created a pressing need for electrical energy storage systems. Among these systems, zinc metal batteries (ZMBs) have shown great potential due to their high theoretical capacity density, low reduction potential, and low-cost anodes. However, the large-scale application of rechargeable ZMBs has been limited by the well-known issues of dendrite growth and adverse side reactions in zinc metal electrodes[1]. Various strategies have been proposed to overcome the challenges associated with zinc dendrite growth, including substrate structure optimization, surface modification of zinc metal, and design of novel electrolytes. Among these approaches, constructing a protective layer on the zinc anode surface is a promising strategy due to its simplicity and cost-effectiveness. A dense polyamide coating layer, prepared by the doctor blading method, has been developed to regulate the aqueous Zn deposition behavior by elevating the nucleation barrier and restricting the 2D diffusion of Zn2+ ions [2]. Additionally, an Al2O3 coating layer has been developed using atomic layer deposition to improve the rechargeability of Zn anodes for zinc-ion batteries (ZIBs)[3]. However, these dense layers face a significant challenge to ionic conductivity due to the high energy barrier that hinders the diffusion of Zn2+ ions, leading to poor battery performance, especially under high current densities. Herein, we report utilizing an electrospun polyimide network (noted as PI-Zn hereafter) to resolve this dilemma. The polyimide (PI) chain has numerous carbonyl oxygen atoms that function as electron donors, supported by theoretical calculations (Fig.1). These atoms can establish stable bonds with Zn ions, thereby reducing the activation energy for de-solvation and enhancing the kinetics of Zn ion deposition. Unlike traditional coating methods, our electrospinning polymers form a fiber-like network structure that provides ion channels, thereby enhancing ion transportation. From the Nyquist plots of symmetrical batteries (Fig. 2), the PI-Zn electrode exhibits smaller charge transfer resistance and lower activation energy compared to the bare Zn electrode. The Transmission X-Ray Microscope results (Fig.3) reveal that the deposition on the bare Zn is heterogeneous and dendritic. In contrast, the PI-Zn electrode presented a smooth surface and dendrite-free morphology. The result demonstrates that the charge transfer ability, de-solvation, and deposition kinetics have been improved by PI-Zn, which further increases the plating uniformity. The stability of the Zn anode was evaluated by long-term galvanostatic cycling of a symmetrical Zn cell (Fig. 4). The cell with bare Zn failed after cycling for 100 h at a current density of 1 mA cm-2 resulting from the short circuit. In comparison, the cell with PI-Zn exhibits a stable polarization voltage and an ultralong cycling life of over 1200 hours, indicating the extremely reversible plating/stripping enabled by the PI network. Further interrogation of the PI-Zn was carried out by stringent deep-discharge tests in symmetrical Zn cells under extremely high current density and capacity of 20 mA cm-2 and 10 mA h cm-2. The result shows that the cell with PI-Zn can cycle for more than 200 hours, two times longer than that with bare Zn, proving the advantages of the unique fiber-like network structure, which provides fast and homogeneous Zn ion transportation. Moreover, the fiber network effectively suppresses the hydrogen evolution reaction, verified by the corrosion test, despite the existence of numerous ion channels. Full prototype cells were assembled to prove the practical applications of our strategy. Two promising cathodes for aqueous ZIBs, MnVOH and MnO2, in two different electrolyte systems (Zn(OTf)2 and ZnSO4) were coupled with bare Zn or PI-Zn. For both systems, the capacity retention and Coulombic efficiency are both significantly improved with the help of PI network. The results are consistent with the lifespan difference of symmetrical Zn cells, as expected due to the same plating and stripping processes on Zn. These findings demonstrate that the strategy presented here stands to both dramatically extend battery cycle life and boost battery performance, a promising approach to solve the anode issues in advanced Zn batteries. Cao, Z., et al., Advanced Energy Materials, 2020. 10(30): p. 2001599. Zhao, Z., et al., Energy & Environmental Science, 2019. 12(6): p. 1938-1949. He, H., et al., Journal of materials chemistry A, 2020. 8(16): p. 7836-7846. Figure 1

Emanuel Ber, Ryan W. Grady, Eric Pop et al.

Abstract Achieving good electrical contacts is one of the major challenges in realizing devices based on atomically thin 2D semiconductors. Several studies have examined this hurdle, but a universal understanding of the contact resistance (Rc) and an underlying approach to its reduction are currently lacking. Here, the classical Rc transmission line model description of contacts to 2D materials is experimentally examined, and a modification based on an additional lateral resistance component, namely, the junction resistance (Rjun) is offered. A combination of transfer length method and contact‐end measurements to characterize contacts to monolayer MoS2 and separate the different Rc components is used. Technology computer‐aided design simulations are also used to study Rc in Fermi‐level pinned and unpinned contacts. This study finds that Rjun is the dominating component of Rc in atomically thin semiconductor devices, and is also responsible for most of the back‐gate bias and temperature dependence. The experimental results help understand the underlying physics of state‐of‐the‐art contact engineering in the context of minimizing Rjun.

Archana Patil, Rekha Patil

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.

Yongmin Baek, Byungjoon Bae, Jeongyong Yang et al.

Abstract Artificial neural networks (ANNs) are widely used in numerous artificial intelligence‐based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN for the artificial intelligence of things (AIoT) and power‐constrained device applications. Therefore, among various ANN algorithms, quantized neural networks (QNNs) have garnered considerable attention because they require fewer computational resources with minimal energy consumption. Herein, an oxide‐based ternary charge‐trap transistor (CTT) that provides three discrete states and non‐volatile memory characteristics are introduced, which are desirable for QNN computing. By employing a differential pair of ternary CTTs, an artificial synaptic segregation with multilevel quantized values for QNNs is demostrated. The approach establishes a platform that combines the advantages of multiple states and robustness to noise for in‐memory computing to achieve reliable QNN performance in hardware, thereby facilitating the development of energy‐efficient AIoT.

Bong Ho Kim, Song‐Hyeon Kuk, Seong Kwang Kim et al.

Abstract The authors demonstrate improved switching voltage, retention, and endurance properties in HfZrOx (HZO)‐based n/p‐ferroelectric field‐effect transistors (FeFETs) via oxygen scavenging. Oxygen scavenging using titanium (Ti) in the gate stack successfully reduce the thickness of interfacial oxide between HZO and Si and the oxygen vacancy at the bottom interface of the HZO film. The n/p‐FeFETs with scavenging exhibit an immediate read‐after‐write with stable retention property and improved endurance property. In particular, n‐FeFET with scavenging exhibits excellent endurance property that does not show breakdown up to 1010 cycles. The charge trapping model in the n/p‐FeFETs is presented to explain why the effect of oxygen scavenging is more pronounced in n‐FeFET than in p‐FeFET. Finally, further switching voltage scaling potential is estimated by scavenging and HZO thickness scaling. It is believed that this work contributes to the development of low‐power FeFET and the understanding of FeFET operation.

K. Uma, K. Perumal

The clinical and diagnostic levels of medical decision-making may benefit from use of machine learning techniques. Algorithms for feature selection provide an basis about machine learning. The most discriminating health-related traits from the initial feature set may be quickly and effectively identified in a medical context by using feature selection. Choosing the most relevant attributes of data classes and improving classification performance are the two main goals of feature selection algorithms. The process of feature selection not only identifies the most useful characteristics but also aids in lowering the dataset's overall dimensions. Hence in this article, we propose a novel algorithm based on machine learning. Initially, the dataset is collected and preprocessed using normalisation method. The features are extracted using Linear Discriminant Analysis (LDA) and the relevant features are selected using the proposed Swarm Optimized Clustering based Genetic Algorithm (SOC-GA). On the healthcare datasets, we implemented the suggested method into action. Using Random Forest (RF) and Support Vector Machine (SVM) classifiers, performances of chosen feature subsets are assessed based on accuracy. The empirical findings from our studies in this research are competitive in terms of accuracy and outperformed the other well-known feature selection methods. This research offers remedies that might improve the accurate, efficient and trustworthy decision-making process in healthcare systems for targeted medical treatments.

Lingxiao Shan, Qi Liu, Yun Ma et al.

Hybrid metal-dielectric structures, which combine the advantages of both metal and dielectric materials, support high-confined but low-loss magnetic and electric resonances under deliberate arrangements. However, their potential for enhancing magnetic emission has not been explored. Here, we study the simultaneous magnetic and electric Purcell enhancement supported by a hybrid structure consisting of a dielectric nanoring and a silver nanorod Such a structure enables low Ohmic loss and highly-confined field under the mode hybridization of magnetic resonances on nanoring and electric resonances on nanorod in the optical communication band. So, the 60-fold magnetic Purcell enhancement and 45-fold electric Purcell enhancement can be achieved simultaneously with $>95\%$ of the radiation transmitted to far field. The position of emitter has a several-ten-nanometer tolerance for sufficiently large Purcell enhancement, which brings convenience to experimental fabrications. Moreover, an array formed by this hybrid nanostructure can further enhance the magnetic Purcell factors. The findings provide a possibility to selectively excite the magnetic and electric emission in integrated photon circuits. It may also facilitate brighter magnetic emission sources and light-emitting metasurfaces in a simpler arrangement.

Pawel Pieranski, Maria Helena Godinho

The persistent quasi-planar nematic texture known also as the dowser texture is characterized by a 2D unitary vector field d. We show here that the dowser texture is sensitive, in first order, to electric fields. This property is due to the flexo-electric polarisation P collinear with d expected from R.B. Meyer's considerations on flexo-electricity in nematics. It is pointed out that due to the flexo-electric polarisation nematic monopoles can be manipulated by electric fields of appropriated geometry.