Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and c...
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We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree-Fock equations for many particles interacting both with each other and with an extern...
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We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree-Fock equations for many particles interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator; (ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many particles); and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the two-dimensional model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the “plain” gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii)—the multiple-mode elimination—may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be employed by other iterative methods, especially those handling hard-core-type interaction of many particles.
Magnetic actuators require reproducible, small-scale approaches for controlling the magnetic field gradient and direction used to drive the actuation. Here a system of two control magnets is designed to generate the l...
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Pristine van der Waals (vdW) heterostructures formed between two-dimensional (2D) and other materials can be used to create optical and electronic devices. However, the weak vdW forces alone do not allow direct physic...
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The non-ionizing and penetrative characteristics of terahertz(THz)radiation have recently led to its adoption across a variety of *** effectively utilize THz radiation,modulators with precise control are *** most rece...
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The non-ionizing and penetrative characteristics of terahertz(THz)radiation have recently led to its adoption across a variety of *** effectively utilize THz radiation,modulators with precise control are *** most recent THz modulators manipulate the amplitude,frequency,or phase of incident THz radiation,considerably less progress has been made toward THz polarization *** methods for polarization control suffer from high driving voltages,restricted modulation depth,and narrow band capabilities,which hinder device performance and broader ***,an ideal THz modulator that offers high modulation depth along with ease of processing and operation is *** this paper,we propose and realize a THz metamaterial comprised of microelectromechanical systems(MEMS)actuated by the phase-transition material vanadium dioxide(VO_(2)).Simulation and experimental results of the three-dimensional metamaterials show that by leveraging the unique phase-transition attributes of VO_(2),our THz polarization modulator offers notable advancements over existing designs,including broad operation spectrum,high modulation depth,ease of fabrication,ease of operation condition,and continuous modulation *** enhanced features make the system a viable candidate for a range of THz applications,including telecommunications,imaging,and radar systems.
Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthet...
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Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.
Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of co...
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Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.
electrically manipulating magnetic moment by spin-orbit torque (SOT) has great potential applications in magnetic memories and logic devices. Although there have been rich SOT studies on magnetic heterostructures, low...
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Correlated electron RAM (CeRAM) device test structures utilizing C-doped NiO were fabricated and electrically characterized to determine functionality in extreme environments. CeRAM devices were demonstrated to repeat...
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Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms ...
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ISBN:
(数字)9798350360585
ISBN:
(纸本)9798350360592
Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.
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