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Found 4 papers, 2 papers with code
Neural Lithography: Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Photolithography Simulator
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.
On the use of deep learning for phase recovery
Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing.
High-performance real-world optical computing trained by in situ model-free optimization
Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps.
Blending Diverse Physical Priors with Neural Networks
Such generalization would require an architecture that can adapt to variations in the correctness of the physics, or in the quality of training data.
