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

Yuhang Zhao, Kunzi Han, Heng Jin et al.

Abstract Carbon nanotube field-effect transistors (CNTFETs) have demonstrated superior performance in integrated devices compared to conventional metal–oxide–semiconductor field-effect transistors (MOSFETs), with optimizations achieved through precise control of chirality, nanotube count, and channel length scaling. However, while these parameters significantly influence device characteristics, the effects of gate oxide engineering and strain-induced field modifications remain incompletely understood, requiring further investigation to fully exploit the potential of CNTFETs. In this study, we systematically investigate these phenomena by combining current transport modeling with first-principles calculations. Our analysis reveals that gate oxide thinning enhances drain–source current by improving gate control, but necessitates high- $$\kappa$$ κ dielectrics (≥ 25) to suppress direct tunneling leakage through Poisson’s equation solutions. At $$\kappa =25{\varepsilon }_{0}$$ κ = 25 ε 0 , we achieved optimized FET metrics: saturation current (12  $$\upmu\mathrm A$$ μ A ), transconductance (277  $$\upmu\mathrm S$$ μ S ), and gate–source capacitance (103.4 $$\text{aF}$$ aF ), representing improvements over conventional SiO2-based designs. Furthermore, under uniaxial radial compressive strain ( $${\varepsilon }_{\text{yy}}$$ ε yy  = 0.66), the saturation current increases by ~10 times compared to the unstrained case due to decreasing bandgap, while the threshold voltage and the subthreshold swing increase by ~2 times as well, lowering transistor switching efficiency and increasing power consumption. Therefore, strain engineering can provide a valid approach for performance-targeted design: high-frequency power amplifiers with large strains and low-power logic gates with small strains.

Mehdi Otmani, Ayssar Serhan, Jean-Daniel Arnould et al.

This paper extends the quasi-load insensitive (QLI) Class-E Doherty power amplifier (PA) design methodology to address Doherty PA combiners with complex load impedance trajectories. Additionally, the QLI operation is analyzed for generalized class-E output matching networks with input series inductors and finite DC-feed inductors. We demonstrate that the QLI class-E Doherty operation can be achieved for various Doherty combiners by selecting the appropriate combination of class-E outputs matching network resonance factors and input series inductances. Moreover, a modified class-E output network is proposed to overcome the frequency limitation that might be caused by the class-E network resonance factor choice. To validate the proposed methodology, two 40 W Doherty PAs are designed and simulated using commercial GaN HEMT transistors achieving more than 70% efficiency over a 6 dB output power back-off at 3.8 GHz.

Orazio Aiello

This paper deals with a standard cell-based analog-in-concept pW-power building block as a comparator and a wake-up oscillator. Both topologies, traditionally conceived as an analog building block made by a custom process and supply voltage-dependent design flow, are designed only by using digital gates, enabling them to be automated and fully synthesizable. This further results in supply voltage scalability and regulator-less operation, allowing direct powering by an energy harvester without additional ancillary circuit blocks (such as current and voltage sources). In particular, the circuit similarities in implementing a rail-to-rail dynamic voltage comparator and a relaxation oscillator using only digital gates are discussed. The building blocks previously reported in the literature by the author will be described, and the common root of their design will be highlighted.

Kunling Peng, Chenjian Fu, Yunzhen Du et al.

Abstract This study systematically explores the electrical and thermal properties of Cd₃P₂ by alloying it with the Dirac material Cd₃As₂, employing a combined experimental and theoretical approach. The findings demonstrate three distinct characteristics of this solid solution system: i) The continuous solid solution formation between Cd₃P₂ and Cd₃As₂ enables the tuning of the band structure. ii) Increasing As content leads to a reduction in effective mass, decreased deformation potential, and a substantial enhancement in carrier mobility. iii) The system exhibits phosphorus vacancy generation, which creates donor levels within the band gap and consequently impacts thermoelectric performance. Specifically, an ultrahigh mobility exceeding 7 × 103 cm2 V−1 s−1 is achieved in Cd₃PAs. This substantial improvement in mobility across the entire temperature range resulted in a twofold increase in the power factor and a marked enhancement in thermoelectric performance, particularly in the low‐temperature region. These results provide foundational insights into the thermoelectric behavior governed by the interplay between the semiconductor Cd₃P₂ and the Dirac material Cd₃As₂, establishing a framework for further research and performance optimization of this solid solution system.

Mingxing Cao, Zhigao Zhang, Jian He et al.

Abstract Tungsten telluride (WTe2) and silver telluride (Ag2Te) are recently developed magnetoresistive materials, and bulk composites of these materials would be extremely advantageous in improving the magnetoresistance characteristics of the individual components and expanding their applications. In this study, previously developed synthesis methods for WTe2 and Ag2Te are applied to effectively engineer WTe2 and Ag2Te bulk composites. Introducing 10% Ag2Te in the WTe2 matrix improves the magnetoresistance and lowers the critical magnetic field and higher onset temperature relative to those of pure‐phase WTe2. The relationship between the magnetoresistance performance and Ag2Te content is further explored using simulations. The onset temperature and critical magnetic field follow the Kohler rule based on resistance calculations. The excellent composite magnetoresistance of these materials will find applications in the field of electronics.

R. Winkler, U. Zülicke

Electric and magnetic multipole densities in crystalline solids, including the familiar electric dipole density in ferroelectrics and the magnetic dipole density in ferromagnets, are of central importance for our understanding of ordered phases in matter. However, determining the magnitude of these quantities has proven to be conceptually and technically difficult. Here we present a universally applicable approach, based on projection operators, that yields gauge-invariant absolute measures for all types of electric and magnetic order in crystals. We demonstrate the utility of the general theory using concrete examples of electric and magnetic multipole order in variants of lonsdaleite and diamond structures. Besides the magnetic dipole density in ferromagnets, we also consider, e.g., the magnetic octupole density in altermagnets. The robust method developed in this work lends itself to be incorporated into the suite of computational materials-science tools. The multipole densities can be used as thermodynamic state variables including Landau order parameters.

S. -Z. Wang, S. -B. Wang, Z. -J. Tao et al.

The response of an atom to external electric and magnetic fields can reveal fundamental atomic properties. It has long been verified that, in a static magnetic field, those atomic energy levels with hyperfine interactions shift according to the Breit-Rabi formula, which introduces nonlinear dependence on the magnetic field. On the other hand, the corresponding Breit-Rabi dependence on a static electric field has not been observed before due to a combination of experimental challenges. Here we precisely measure the Stark shift of the $6s^2\ ^1S_0\ \leftrightarrow\ 6s6p\ ^1P_1$ transition of $^{171}$Yb ($I$ = 1/2) with cold atoms held by an optical dipole trap in a static electric field up to 120 kV/cm. We observe the electric Breit-Rabi effect displaying high-order ($E^4$ and $E^6$) DC Stark shifts. These effects arise from the influence of the strong electric field on hyperfine interactions.

Makoto Nagata, Takuji Miki

Semiconductor integrated circuit (IC) chips are regularly exposed to physical attacks and faced to the compromise of information security. An attacker leverages Si substrate backside as the open surface of an IC chip in flip-chip packaging and explores the points of information leakage over the entire backside without being hampered by physical obstacles as well as applying invasive treatments. Physical side channels (SCs), e.g., voltage potentials, current flows, electromagnetic (EM) waves, and photons, are transparent through Si substrate and attributed to the operation of security ICs. An attacker measures SCs using probes as well as antennas and correlates them with secret information, such as secret key bytes, used in a cryptographic processor or analog quantities at the frontend of Internet of Things (IoT) gadgets. This article defines and elucidates the emerging threats of Si-substrate backside attacks on flipped IC chips, demonstrates attacks and proposes countermeasures.

Ahmad Khairi, Amir Laufer, Ilia Radashkevich et al.

System considerations, circuit architecture, and design implementation of wireline and linear optics transceivers capable of supporting data-rates beyond 200 Gb/s are presented. We showcase the silicon results of a transceiver designed in the advanced 3-nm CMOS process, which supports long-reach channels with up to 40 dB of loss at Nyquist. These results demonstrate the technology’s benefits of doubling the data rate of transceivers while achieving efficiency gains in power consumption and silicon area. This article highlights several key circuits architecture, such as hybrid continuous-time linear equalizer, inductive peaking clock routing, and one stage TX driver based on grounded switches. The proof-of-concept demonstration of 224 Gb/s with linear optics opens the avenue for power-efficient, low-latency future optical communication. This is crucial for high-performance computing (HPC) networking as well as emerging applications in high-end FPGA.

Wenyu Sun, Jinshan Yue, Yifan He et al.

The computing-in-memory (CIM) technique is emerging with the evolvement of big data and artificial intelligence (AI) application. The manuscript presents a systematic review of existing CIM works in a bottom-up view from circuit to application. Various types of CIM circuits based on different volatile/nonvolatile devices are introduced. The micro CIM architectures are illustrated to support multibit precision computation. After that, several types of processor-level CIM chips are analyzed to reveal the system architecture design considerations. The corresponding CIM tool chains and applications beyond AI applications are also introduced. From circuit to application levels, this manuscript analyzes the design tradeoffs, remained challenges, and possible future design trends at different design hierarchies of CIM processors.

G. Ganapathy, Sujatha Jamuna Anand, M. Jayaprakash et al.

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in an IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16 % for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

Shubham Tanwar, Ruben Millan‐Solsona, Sara Ruiz‐Molina et al.

Abstract Electrolyte‐gated organic transistors (EGOTs) leveraging organic semiconductors' electronic and ionic transport characteristics are the key enablers for many biosensing and bioelectronic applications that can selectively sense, record, and monitor different biological and biochemical processes at the nanoscale and translate them into macroscopic electrical signals. Understanding such transduction mechanisms requires multiscale characterization tools to comprehensively probe local electrical properties and link them with device behavior across various bias points. Here, an automated scanning dielectric microscopy toolbox is demonstrated that performs operando in‐liquid scanning dielectric microscopy measurements on functional EGOTs and carries out extensive data analysis to unravel the evolution of local electrical properties in minute detail. This paper emphasizes critical experimental considerations permitting standardized, accurate, and reproducible data acquisition. The developed approach is validated with EGOTs based on blends of organic small molecule semiconductor and insulating polymer that work as accumulation‐mode field‐effect transistors. Furthermore, the degradation of local electrical characteristics at high gate voltages is probed, which is apparently driven by the destruction of local crystalline order due to undesirable electrochemical swelling of the organic semiconducting material near the source electrode edge. The developed approach paves the way for systematic probing of EGOT‐based technologies for targeted optimization and fundamental understanding.

V. Gorbounov, A. Kazakov

A classic problem in data analysis is studying the systems of subsets defined by either a similarity or a dissimilarity function on $X$ which is either observed directly or derived from a data set. For an electrical network there are two functions on the set of the nodes defined by the resistance matrix and the response matrix either of which defines the network completely. We argue that these functions should be viewed as a similarity and a dissimilarity function on the set of the nodes moreover they are related via the covariance mapping also known as the Farris transform or the Gromov product. We will explore the properties of electrical networks from this point of view. It has been known for a while that the resistance matrix defines a metric on the nodes of the electrical networks. Moreover for a circular electrical network this metric obeys the Kalmanson property as it was shown recently. We will call such a metric an electrical Kalmanson metric. The main results of this paper is a complete description of the electrical Kalmanson metrics in the set of all Kalmanson metrics in terms of the geometry of the positive Isotropic Grassmannian whose connection to the theory of electrical networks was discovered earlier. One important area of applications where Kalmanson metrics are actively used is the theory of phylogenetic networks which are a generalization of phylogenetic trees. Our results allow us to use in phylogenetics the powerful methods of reconstruction of the minimal graphs of electrical networks and possibly open the door into data analysis for the methods of the theory of cluster algebras.

Sumer Makhlouf, Oleg Cojocari, Martin Hofmann et al.

The rapid progress in semiconductor technology has vastly boosted the development of terahertz sources and receivers in terms of compactness, reliability, operation frequency, and output power. In this manuscript, we report on the latest achievements in terahertz sources and receivers and provide a comprehensive overview of their working principles and applications in THz systems.

Yitong Shen, Mohamad Alzayed, Hicham Chaoui

The Proton Exchange Membrane Fuel Cell (PEMFC), known for its efficient energy conversion, minimal electrolyte leakage, and low operating temperature, shows great potential as a clean energy source. However, its lifespan is limited due to degradation during normal operation, which, if uncontrolled, can result in dangerous failures such as explosions. Hence, accurately estimating the remaining useful life (RUL) is vital. In this research, a combined prediction method using genetic algorithms (GA) and nonlinear autoregressive neural networks (NARX) with external inputs is proposed. The method's performance was trained and validated using the 2014 IEEE PHM Data Challenge dataset, and it was compared to two commonly used artificial neural network algorithms: GA-based backpropagation neural network (GA-BPNN) and GA-based time delay neural network (GA-TDNN). The findings demonstrate that the proposed approach surpasses the other two artificial neural network algorithms in terms of prediction accuracy. Although GA is known for its computational requirement, optimization is performed offline. Once optimal neural network (NN) hyper-parameters are determined, the optimized NN is used online for RUL prediction.

Rémi Lacombe, Nikolce Murgovski, Sébastien Gros et al.

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. We propose a hierarchical control framework to solve this problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions together with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.

Teruo Matsushita

Cleber F.N. Marchiori, Giane B. Damas, C. Moyses Araujo

Porphyrins constitute a class of attractive materials for harvesting sunlight and promote chemical reactions following their natural activity for the photosynthetic process in plants. In this work, we employ an in-silico design strategy to propose novel porphyrin-based materials as photocatalysts for hydrogen evolution reaction (HER). More specifically, a set of meso-substituted porphyrins with donor-acceptor architecture are evaluated within the density functional theory (DFT) framework, according to these screening criteria: i) broad absorption spectrum in the ultraviolet–visible (UV–Vis) and near infrared (NIR) range, ii) suitable redox potentials to drive the uphill reaction that lead to molecular hydrogen formation, iii) low exciton binding free energy (Eb), and iv) low hydrogen binding free energy (ΔGH), a quantity that should present low HER overpotentials, ideally ΔGH = 0. The outcomes indicate that the Se-containing compound, where the donor ligands are attached to the porphyrin core by the spacer, outstands as the most promising candidate that is presented in this work. It displays a broad absorption in the visible and NIR regions to up to 1000 nm, suitable catalytic power, low Eb (in special in high dielectric constant environment, such as water) and the lowest ΔGH = +0.082 eV. This is comparable, in absolute values, to the value exhibited by platinum (ΔGH = −0.10 eV), one of the most efficient catalysts for HER.

P. Hema, E. Sathish, M. Maheswari et al.

A challenge for “Big Data” in the chemical production industry is not only to evaluate file storage but also to use online information to improve process performance. It should be spectral, vibration, thermal, and other sensors are more and more widely available. In today's harsh industrial conditions, accurate and reliable reviews or product quality assessments are critical. To predict important attribute factors utilizing quantifiable signals, information soft sensors dependent on Projection to Latent Structure (PLS) techniques are frequently used. However, due to changes in equipment, raw material, sensors, or management, most operations are carried out under real and stable conditions. The structure of the flexible sensors must be maintained at regular intervals. Reconstruction of the method using more recent sensor primary data focus of current design maintenance techniques, such as mobile window updates and recursive updates within the enterprise. In situations where data were collected with extremes, downtimes, and other transients in the non-stationary phase, this strategy was not sufficiently resilient. An alternative model update strategy was reviewed as part of this study. To assess the effectiveness of the current soft sensor approach, they modified two Key Performance Indicators (KPIs). The residue-dependent forecast KPI identifies long-term forecast damping models using a filtered estimation error. The KPI dependent on T2 would be a forecast KPI that checks the system's speculations to the expected original data. This updated strategy is effective in improving predictive accuracy without completely reconstructing the PLS model using research papers using industrial operations information. Finally, the KPI attributes and model upgrade mechanism could be used together. The researchers demonstrated that this update technique significantly improved the accuracy of the PLS soft detector predictions through the emulation of live behavior using industrial data. The configuration technique also made it possible to quickly identify underlying issues in situations where the original sample was ideal as well as informed engineers that a new method needed to be built.

Pandav Kiran Maroti, Sanjeevikumar Padmanaban, Mahajan Sagar Bhaskar et al.

Today, the Internal Combustion Engine (ICE) is gradually being replaced by electric motors, which results in higher efficiency and low emission of greenhouse gases. The electric vehicle either works wholly or partially on electrical energy generated from batteries and ultra-capacitors. The battery or ultra-capacitor is either charged from the AC supply connected to a grid line in a plug-in electric vehicle or from ICE in a hybrid electric vehicle. Alternatively, the battery charges from the traction motor by regenerative braking. In the reverse direction, the energy from the battery or ultra-capacitor is injected into the AC grid line in the plug-in electric vehicle. Power electronic converters play a vital role in the conversion process from grid line to traction motor and in the reverse direction. In this paper, the role of power electronics converters in an electric vehicle is elaborated. The bidirectional DC-DC converter plays a vital role in the power conversion process of electric vehicles. The existing bidirectional DC-DC converter topologies are discussed with a comprehensive review, comparison, and application. Additionally, the advancement in power electronics converters to improve the efficiency and reliability of the vehicular system is elaborated.