Inverse designed metalenses with extended depth of focus

Abstract

Extended depth of focus (EDOF) lenses are important for various applications in computational imaging and microscopy. In addition to enabling novel functionalities, EDOF lenses can alleviate the need for stringent alignment requirements for imaging systems. Existing EDOF lenses, however, are often inefficient or produce an asymmetric point spread function (PSF) that blurs images. Inverse design of nanophotonics, including metasurfaces, has generated strong interest in recent years owing to its potential for generating exotic and innovative optical elements, which are generally difficult to model intuitively. Using adjoint optimization-based inverse electromagnetic design, in this paper, we designed a cylindrical metasurface lens operating at ~ 625nm with a depth of focus exceeding that of an ordinary lens. We validated our design by nanofabrication and optical characterization of silicon nitride metasurface lenses (with lateral dimension of 66.66 μm) with three different focal lengths (66.66 μm,100 μm,133.33 μm). The focusing efficiencies of the fabricated extended depth of focus metasurface lenses are similar to those of traditional metalenses.

arXiv commentACS Photonics, 2020

References in corpus (10)

• Dielectric Metasurfaces for Complete Control of Phase and Polarization with Subwavelength Spatial Resolution and High Transmission
• Subwavelength-thick Lenses with High Numerical Apertures and Large Efficiency Based on High Contrast Transmitarrays
• Metasurface Optics for Full-color Computational Imaging
• High-NA Achromatic Metalenses by Inverse Design
• Low Contrast Dielectric Metasurface Optics
• Inverse design of large-area metasurfaces
• Topology Optimized Multi-layered Meta-optics
• Topology optimization of freeform large-area metasurfaces
• Simultaneous achromatic and varifocal imaging with quartic metasurfaces in the visible
• Inverse design of optical elements based on arrays of dielectric spheres

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