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  1. The combination of photonic integrated circuits and free-space metaoptics has the ability to untie technological knots that require advanced light manipulation due to their conjoined ability to achieve strong light–matter interaction via wave-guiding light over a long distance and shape them via large space-bandwidth product. Rapid prototyping of such a compound system requires component interchangeability. This represents a functional challenge in terms of fabrication and alignment of high-performance optical systems. Here, we report a flexible and interchangeable interface between a photonic integrated circuit and the free space using an array of low-loss metaoptics and demonstrate multifunctional beam shaping at a wavelength of 780 nm. We show that robust and high-fidelity operation of the designed optical functions can be achieved without prior precise characterization of the free-space input nor stringent alignment between the photonic integrated chip and the metaoptics chip. A diffraction limited spot of ∼3 μm for a hyperboloid metalens of numerical aperture 0.15 is achieved despite an input Gaussian elliptical deformation of up to 35% and misalignments of the components of up to 20 μm. A holographic image with a peak signal-to-noise ratio of >10 dB is also reported. 
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  2. Abstract We report an inverse-designed, high numerical aperture (∼0.44), extended depth of focus (EDOF) meta-optic, which exhibits a lens-like point spread function (PSF). The EDOF meta-optic maintains a focusing efficiency comparable to that of a hyperboloid metalens throughout its depth of focus. Exploiting the extended depth of focus and computational post processing, we demonstrate broadband imaging across the full visible spectrum using a 1 mm, f/1 meta-optic. Unlike other canonical EDOF meta-optics, characterized by phase masks such as a log-asphere or cubic function, our design exhibits a highly invariant PSF across ∼290 nm optical bandwidth, which leads to significantly improved image quality, as quantified by structural similarity metrics. 
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