Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Two-Photon Neural Lithography Simulator
Name
3610548.3618251.pdf
Size
21.15 MB
Format
Adobe PDF
Checksum (MD5)
6a2f63ece380a9e8cc926511d3b8d4c9
Author(s)
Zheng, Cheng
Zhao, Guangyuan
So, Peter
Date Issued
December 10, 2023
Publisher
ACM|SIGGRAPH Asia 2023 Conference Papers
Citation
Zheng, Cheng, Zhao, Guangyuan and So, Peter. 2023. "Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Two-Photon Neural Lithography Simulator."
Version
Final published version
Abstract
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. The source code for this work is available on the project page: https://neural-litho.github.io.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
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DOI of Published Version
https://doi.org/10.1145/3610548.3618251
