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We introduce neural lithography to address the 'design-to-manufacturing' gap
in computational optics. Computational optics with large design degrees of
freedom enable advanced functionalities and performance beyond traditional
optics. However, the existing design approaches often overlook the numerical
modeling of the manufacturing process, which can result in significant
performance deviation between the design and the fabricated optics. To bridge
this gap, we, for the first time, propose a fully differentiable design
framework that integrates a pre-trained photolithography simulator into the
model-based optical design loop. Leveraging a blend of physics-informed
modeling and data-driven training using experimentally collected datasets, our
photolithography simulator serves as a regularizer on fabrication feasibility
during design, compensating for structure discrepancies introduced in the
lithography process. We demonstrate the effectiveness of our approach through
two typical tasks in computational optics, where we design and fabricate a
holographic optical element (HOE) and a multi-level diffractive lens (MDL)
using a two-photon lithography system, showcasing improved optical performance
on the task-specific metrics.
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