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Hydrogel-based diffractive optical elements (hDOEs) using rapid digital photopatterning.

ABSTRACT: Hydrogels, due to their optical transparency and biocompatibility, have emerged as an excellent alternative to conventional optical materials for biomedical applications. Advances in microfabrication techniques have helped convert conventional hydrogels into optically functional materials such as hydrogel-based diffraction optical elements (hDOEs). However, key challenges related to device customization and ease/speed of fabrication need to be addressed to enable widespread utility and acceptance of hDOEs in the field. Here, we report rapid printing of customized hDOEs on polyethylene glycol diacrylate (PEGDA) hydrogel using digital photopatterning; a novel method that combines simulated computer-generated hologram (SCGH) and projection photolithography. To showcase the versatility of this approach, a range of hDOEs are demonstrated, including 1D/2D diffraction gratings, Dammann grating, Fresnel zone plate (FZP) lens, fork-shaped grating and computer-generated hologram (CGH) of arbitrary pattern. Results demonstrate that printed hDOEs exhibit optical performance that is comparable with devices made with conventional materials. This versatile strategy can be potentially implemented with other photosensitive hydrogels to achieve user-defined hDOEs in a time-efficient and cost-effective fashion.

SUBMITTER: Xiong Z

PROVIDER: S-EPMC7939132 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components. Here, we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary, and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is ≥Ni × No, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < Ni × No. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.

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Hydrogel-based diffractive optical elements (hDOEs) using rapid digital photopatterning.

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