Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks

Elena Goi (), Steffen Schoenhardt and Min Gu ()
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Elena Goi: University of Shanghai for Science and Technology
Steffen Schoenhardt: University of Shanghai for Science and Technology
Min Gu: University of Shanghai for Science and Technology

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows for aberration correction, to the imaging of near-transparent biological samples in phase contrast microscopy. Current phase retrieval schemes rely on complex digital algorithms that process data acquired from precise wavefront sensors, reconstructing the optical phase information at great expense of computational resources. Here, we present a compact optical-electronic module based on multi-layered diffractive neural networks printed on imaging sensors, capable of directly retrieving Zernike-based pupil phase distributions from an incident point spread function. We demonstrate this concept numerically and experimentally, showing the direct pupil phase retrieval of superpositions of the first 14 Zernike polynomials. The integrability of the diffractive elements with CMOS sensors shows the potential for the direct extraction of the pupil phase information from a detector module without additional digital post-processing.

Date: 2022
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DOI: 10.1038/s41467-022-35349-4

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