In this paper, we design a type of switchable metasurfaces by employing vanadium dioxide (VO), which possess tunable and diversified functionalities in the terahertz (THz) frequencies. The properly designed homogeneous metasurface can be dynamically tuned from a broadband absorber to a reflecting surface due to the insulator-to-metal transition of VO. When VO is in its insulating state, the metasurface can efficiently absorb the normally incident THz wave in the frequency range of 0.535-1.3 THz with the average absorption of ~97.2%. Once the VO is heated up and switched to its fully metallic state, the designed metasurface exhibits broadband and efficient reflection (>80%) in the frequency range from 0.5 to 1.3 THz. Capitalizing on such meta-atom design, we further extend the functionalities by introducing phase-gradients when VO is in its fully metallic state and consequently achieve polarization-insensitive beam-steering and polarization-splitting, while maintaining broadband absorption when VO is in insulating state.
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http://dx.doi.org/10.1038/s41598-019-41915-6 | DOI Listing |
Nano Lett
January 2025
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
The complete manipulation of Jones matrix phase-channels using metasurfaces brings forth unparalleled possibilities across diverse wavefront modulation applications. Traditionally, achieving independent control over all four phase-channels usually involves the introduction of chirality with multilayer or three-dimensional metasurfaces. Here, we present a general chirality-free method that relies on polarization base transformation with a planar minimalist metasurface, effectively decoupling the four Jones matrix phase-channels, thereby unleashing the fundamental boundaries imposed by conventional linear or circular polarization bases.
View Article and Find Full Text PDFACS Sens
December 2024
UNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Polarization and wavelength multiplexed metalenses address the bulkiness of traditional imaging systems. However, despite progress with numerical simulations and parameter scanning, the engineering complexity of classical methods highlights the urgent need for efficient deep learning approaches. This paper introduces a deep learning-driven inverse design model for polarization-multiplexed metalenses, employing propagation phase theory alongside spectral transfer learning to address chromatic dispersion challenges.
View Article and Find Full Text PDFNano Lett
December 2024
School of Physics and Astronomy, Faculty of Science, Monash University, Melbourne, Victoria 3800, Australia.
Ultrathin and low-loss phase-change materials (PCMs) are highly valued for their fast and effective phase transitions and applications in reconfigurable photonic chips, metasurfaces, optical modulators, sensors, photonic memories, and neuromorphic computing. However, conventional PCMs mostly suffer from high intrinsic losses in the near-infrared (NIR) region, limiting their potential for high quality factor (-factor) resonant metasurfaces. Here we present the design and fabrication of tunable bound states in the continuum (BIC) metasurfaces using the ultra-low-loss PCM SbSe.
View Article and Find Full Text PDFNanophotonics
April 2024
Beijing Key Laboratory of Metamaterials and Devices, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Beijing Advanced Innovation Center for Imaging Theory and Technology, Department of Physics, Capital Normal University, Beijing, 100048, China.
Diffractive deep neural networks ( ) have brought significant changes in many fields, motivating the development of diverse optical computing components. However, a crucial downside in the optical computing components is employing diffractive optical elements (DOEs) which were fabricated using commercial 3D printers. DOEs simultaneously suffer from the challenges posed by high-order diffraction and low spatial utilization since the size of individual neuron is comparable to the wavelength scale.
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