The miniaturization of current image
sensors is largely limited
by the volume of the optical elements. Using a subwavelength-patterned
quasi-periodic structure, also known as a metasurface, one can build
planar optical elements based on the principle of diffraction. Recent
demonstrations of high-quality metasurface optical elements are mostly
based on high-refractive-index materials. Here, we present a design
of low-contrast metasurface-based optical elements. We fabricate and
experimentally characterize several silicon nitride-based lenses and
vortex beam generators. The fabricated lenses achieved beam spots
of less than 1 μm with numerical apertures as high as ∼0.75.
We observed a transmission efficiency of 90% and focusing efficiency
of 40% in the visible regime. Our results pave the way toward building
low-loss metasurface-based optical elements at visible frequencies
using low-contrast materials and extend the range of prospective material
systems for metasurface optics.
Reconfiguration of silicon photonic integrated circuits relying on the weak, volatile thermo-optic or electro-optic effect of silicon usually suffers from a large footprint and energy consumption. Here, integrating a phase-change material, Ge 2 Sb 2 Te 5 (GST) with silicon microring resonators, we demonstrate an energy-efficient, compact, non-volatile, reprogrammable platform. By adjusting the energy and number of free-space laser pulses applied to the GST, we characterize the strong broadband attenuation and optical phase modulation effects of the platform, and perform quasi-continuous tuning enabled by thermooptically-induced phase changes. As a result, a non-volatile optical switch with a high extinction ratio, as large as 33 dB, is demonstrated.
Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2.
Varifocal lenses are essential components of dynamic optical systems with applications in photography, mixed reality, and microscopy. Metasurface optics has strong potential for creating tunable flat optics. Existing tunable metalenses, however, typically require microelectromechanical actuators, which cannot be scaled to large area devices, or rely on high voltages to stretch a flexible substrate and achieve a sufficient tuning range. Here, we build a 1
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