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.
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.
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/models.md):
| Foundry | Process | Latest Version | Latest Dataset | Model Name | Model Tag | Status |
|---|---|---|---|---|---|---|
| ANT | NanoSOI | v6 (Nov 24 2023) | d8 (Feb 6 2023) | ANT_NanoSOI | v6-d8 | Beta |
| ANT | SiN (Upper Edge) | v5 (Jun 3 2023) | d0 (Jun 1 2023) | ANT_SiN | v5-d0-upper | Alpha |
| ANT | SiN (Lower Edge) | v5 (Jun 3 2023) | d0 (Jun 1 2023) | ANT_SiN | v5-d0-lower | Alpha |
| SiEPICfab | SOI | v5 (Jun 3 2023) | d0 (Jun 14 2023) | SiEPICfab_SOI | v5-d0 | Alpha |
New models and foundries are to be added. Usage may change. For additional foundry and process models, feel free to contact us or raise an issue.
Install PreFab via pip:
pip install prefabOr clone the repository and install in development mode:
git clone https://github.com/PreFab-Photonics/PreFab.git
cd PreFab
pip install -e .Use PreFab online through GitHub Codespaces:
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 setupVisit /examples or our Guides to get started with your first predictions.
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.
