Optical parametric oscillators(OPOs) can downconvert the pump laser to longer wavelengths with octave separation via χ^((2)), which is widely used for laser wavelength extension including mid-infrared(MIR) *** a proc...
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Optical parametric oscillators(OPOs) can downconvert the pump laser to longer wavelengths with octave separation via χ^((2)), which is widely used for laser wavelength extension including mid-infrared(MIR) *** a process can be integrated in monolithic resonators, being compact and low in threshold. In this work, we show that the monolithic χ^((2))mini-OPO can also be used for optical frequency comb generation around 2096 nm and enters the boundary of MIR range. A new geometry called an optical superlattice box resonator is developed for this realization with near-material-limited quality factor of 4.0 × 10^(7). Only a continuous-wave near-infrared pump laser is required, with OPO threshold of 80 mW and output power up to 340 mW. Revival temporal profiles are measured at a detectable repetition frequency of 1.426 GHz, and narrow beat note linewidth of less than 10 Hz shows high comb coherence. These results are in good agreement with our simulation for a stable comb generation. Such an OPO-based comb source is useful for carbon dioxide sensing or the mine prospect applications and can be generalized to longer MIR wavelengths for general gas spectroscopy.
Nanoscale magnetic domains with controllable configurations could be used for classical and quantum applications, where the switching of magnetization configurations is an essential operation for information processin...
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Nanoscale magnetic domains with controllable configurations could be used for classical and quantum applications, where the switching of magnetization configurations is an essential operation for information processing. Here, we report that the magnetic domain reorientation in a notched ferromagnetic nanotrack can be realized and effectively controlled by applying uniform magnetic field pulses in a fixed in-plane direction perpendicular to the nanotrack. Our micromagnetic simulation results show that the configurations of magnetic domains in the notched nanotrack can be switched between a head-to-head state and a tail-to-tail state in a reversible manner driven by magnetic field pulses, while it is unnecessary to reverse the direction of the magnetic field. Such a unique magnetic domain reorientation dynamics is found to depend on magnetic parameters and nanotrack geometries. The reorientation dynamics of magnetic domains also depends on the strength and length of the applied magnetic-field pulse. In addition, we point out that the notches at the center of the nanotrack play an important role for the stabilization of the head-to-head and tail-to-tail states during the magnetic domain reorientation. We also qualitatively explain the field-induced reorientation phenomenon with a simplified two-dimensional macrospin model. Our results may make it possible to build spintronic devices driven by a fixed magnetic field. Our findings may also motivate future studies to investigate the classical and quantum applications based on nanoscale magnetic domains.
We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed labor...
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We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed laboratory-frame axis and observe oscillations in the purity at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on the noise anisotropy, orientation, and power spectral density, using a low-frequency fluxonium qubit. Our results elucidate the impact of transverse noise anisotropy on qubit decoherence and may be useful for disentangling charge and flux noise in superconducting quantum circuits.
We synthesized crystalline films of neodymium nickel oxide (NdNiO3), a perovskite quantum material, switched the films from a metal phase (intrinsic) into an insulator phase (electron-doped) by field-driven lithium-io...
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As spin caloritronic measurements become increasingly common techniques for characterizing material properties, it is important to quantify potentially confounding effects. We report measurements of the Nernst-Ettings...
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Artificial neural networks (ANNs) have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these ANNs for image processing demand substantial computational resources, ...
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We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip...
This paper investigates the secrecy performance of full-duplex cooperative vehicular relaying networks under passive eavesdropping attacks in the presence of co-channel interference. In the considered system model, a ...
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Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute comp...
Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application.
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