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Metasurface has garnered extensive attention across multiple disciplines owing to its profound capability in electromagnetic (EM) manipulations. To determine its EM characteristics accurately, full-wave simulations are essential. These simulations necessitate significant amounts of time and memory resources, hindering the efficiency of the design process. In this article, we propose MetaPhyNet, a novel physics-driven neural network based on temporal coupled-mode theory (CMT) to address the challenges of low efficiency and high memory consumption in large-scale metasurface design. In the proposed approach, a surrogate model is developed to achieve rapid prediction of the EM responses of ultra-large-scale metasurfaces. In comparison with the full-wave EM simulation, the proposed model reduces the simulation time of the ultra-large-scale metasurface by up to two orders of magnitude and the memory consumption by more than two orders of magnitude. Our proposed approach aims to enhance the efficiency and intelligence in metasurface design by leveraging the principles of CMT within a neural network framework. Through this innovative integration of physics-based modeling and machine learning, we seek to achieve significant advancements in the design efficiency of metasurfaces. We apply the proposed model to optimize the design of two metasurface absorbers to showcase the effectiveness of our proposed approach. Simulations and experimental results are provided to demonstrate the value and impact of our approach in addressing existing challenges in full-wave EM simulation-based design optimizations of metasurfaces.

Optical nanoantennas have attracted significant attention over the past decades, owing to their exceptional capabilities in terms of light manipulation and versatile optical applications. Recently, active nanoantennas have been developed by introducing phase change materials, to achieve specific tunable electromagnetic responses. However, most of these attempts only function with ‘ON’/‘OFF’ states or switch in a few discrete states, significantly restricting the application in dynamic tunability. Thus far, the continuous and reversible modulation of optical nanoantennas has not been sufficiently explored. In this article, we experimentally demonstrate a continuously and reversibly electro-tunable optical nanoantenna, by integrating an asymmetric gold nanodisk dimer array with a vanadium dioxide (VO _2 ) film and graphene thin film. By accurately controlling the applied electrical current, the Joule heat generated in the graphene film excites the metal-insulator phase transition of VO _2 , and the refractive index of VO _2 exhibits a relatively large variation. When VO _2 is in the insulating phase, we observe an apparent resonance dip in the reflection spectrum, which is attributed to a hybrid mode originating from the gap plasmon in the dimers and localized surface plasmon (LSP) resonance excited at the edge of the nanodisks. Meanwhile, owing to the coupling between two asymmetric LSP resonances in the neighboring nanodisks, the reflected peak based on the Fano effect is realized. However, once VO _2 is in the metallic phase, the hybrid mode becomes weaker and red-shifted, and the Fano effect disappears. Thereafter, the continuous and reversible electro-modulation of the nanoantenna features, including the resonant wavelength, resonant intensity, and quality factors ( Q ), are experimentally verified in the optical communication region, by varying the applied electrical current in the hybrid structure. To further increase the modulation range of these properties, we scan the gap size and structural asymmetry parameter of the nanodisk dimer. The results show that, with a smaller gap size, the resonant intensity of the nanoantenna is stronger. When the structural asymmetry parameter increases, the resonant wavelength is redshifted. We expect that such continuously and reversibly electro-tunable nanoantennas will stimulate various applications in optical communication systems, tunable photoelectric sensors, and beyond.

MAXI J1820+070 is a newly discovered black hole X-ray binary, whose dynamical parameters, namely the black hole mass, the inclination angle, and the source distance, have recently been estimated. Insight-HXMT have observed its entire outburst from 2018 March 14. In this work, we attempted to estimate the spin parameter a*, using the continuum-fitting method and applying a fully relativistic thin disk model to the soft-state spectra obtained by Insight-HXMT. It is well known that a* is strongly dependent on three dynamical parameters in this method, and we have examined two sets of parameters. Adopting our preferred parameters: M = , i = 63° ± 3°, and D = 2.96 ± 0.33 kpc, we found a slowly spinning black hole of a* = 0.14 ± 0.09 (1σ), which gives a prograde spin parameter as the majority of other systems show. It is also possible for the black hole to have a retrograde spin (less than 0) if different dynamical parameters are taken. [ABSTRACT FROM AUTHOR]