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Artificial nanofabrication of integrated photonic circuits using deep learning

Project description


PreFab logo

PreFab leverages deep learning to model fabrication-induced structural variations in integrated photonic devices. Through this virtual nanofabrication environment, we uncover valuable insights into nanofabrication processes and enhance device design accuracy.


PreFab accurately predicts process-induced structural alterations such as corner rounding, washing away of small lines and islands, and filling of narrow holes in planar photonic devices. This enables designers to quickly prototype expected performance and rectify designs prior to nanofabrication.

Example of PreFab prediction


PreFab automates corrections to device designs, ensuring the fabricated outcome aligns with the original design. This results in reduced structural variation and performance disparity from simulation to experiment.

Example of PreFab correction


PreFab accommodates unique predictor and corrector models for each photonic foundry, regularly updated based on recent fabrication data. Current models include (see full list on docs/

Foundry Process Latest Version Latest Dataset Model Name Model Tag
ANT NanoSOI ANF1 (May 6 2023) d9 (Feb 6 2024) ANT_NanoSOI ANF1-d9
ANT SiN ANF1 (May 6 2023) d1 (Jan 31 2024) ANT_SiN ANF1-d1

New models and foundries are to be regularly added. Usage may change. For additional foundry and process models, feel free to contact us.


Install PreFab via pip:

pip install prefab

Or clone the repository and install in development mode:

git clone
cd PreFab
pip install -e .

Getting Started

Account setup

Before you can make PreFab requests, you will need to create an account.

To link your account, you will need a token. You can do this by running the following command in your terminal. This will open a browser window where you can log in and authenticate your token.

python3 -m prefab setup


Visit /docs/examples or our docs to get started with your first predictions.

Performance and Usage

PreFab models are served via a serverless cloud platform. Please note:

  • 🐢 CPU inference may result in slower performance. Future updates will introduce GPU inference.
  • 🥶 The first prediction may take longer due to cold start server loading. Subsequent predictions will be faster.
  • 😊 Be considerate of usage. Start small and limit usage during the initial stages. Thank you!


This project is licensed under the LGPL-2.1 license. © 2024 PreFab Photonics.

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