Neural Lithography

Repo for the project - Neural Lithography: Close the Design to Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Photolithography Simulator

Project Page | ArXiv| MIT News

Cheng Zheng^✉,†,${^1}$, Guangyuan Zhao^✉,†,$^{2}$, Peter So $^{1}$. † denotes equal contribution; ✉ denotes corresponding author
$^1$ MIT, $^2$ CUHK.

📌 Related paper accepted to SIGGRAPH ASIA 2023.

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1. What We Contribute?

TL;DR: 1️⃣ A real2sim pipeline to quantitatively digitalize a real-world lithography system into a high-fidelity neural lithography digital twin; 2️⃣ A fully-differentiable two-stage design-manufacturing co-optimization framework to bridge the design-to-manufacturing gap in computational optics.

To expand, this work answers two fundmental questions in computational optics (including computational lithography). See our definition of computational optics in xxx.

This work answers two fundmental questions in computational optics (including computational lithography):

1. What is the "elephant in the room" in Computational Lithography?

• High-fidelity photolithography simulator. | "No matter how good we can advance the computational (inverse) lithography algorithm, the performance bound is grounded in the fidelity of the lithography simulator."

2. What hinders the progress of end to end differentiable design computational optics?

• One should be the Design to Manufacturing gap. | "Yes you can design a perfect lens, but you cannot guarantee the post-manufacturing performance."

Accordingly, our work tackles the above questions and opens up two exciting research directions:

1. Real2Sim learning for 3D modelling the fabrication outcome of any real-world photolithography system.

2. Close the Design-to-manfuctuting gap via co-optimizing the manufacturiability and the task design with two intersected differentiable simulators (Litho + Task).

2. How to Use this Repo?

See details in code.md.

3. TODO

This repo is under active development with more features to be released.

• Get the first release of the code with code for experiments.
• Create a simulator to be used as a sandbox.
• Neural Litho 2.0. Please stay tuned.

4. Citation

If you find our code or any of our materials useful, please cite our paper:

@article{zheng2023neural,
            title={Neural Lithography: Close the Design-to-Manufacturing Gap in Computational Optics with a'Real2Sim'Learned Photolithography Simulator},
            author={Zheng, Cheng and Zhao, Guangyuan and So, Peter TC},
            journal={arXiv preprint arXiv:2309.17343},
            year={2023}
            }

@inproceedings{zheng2023close,
            title={Close the Design-to-Manufacturing Gap in Computational Optics with a'Real2Sim'Learned Two-Photon Neural Lithography Simulator},
            author={Zheng, Cheng and Zhao, Guangyuan and So, Peter},
            booktitle={SIGGRAPH Asia 2023 Conference Papers},
            pages={1--9},
            year={2023}
}