prefab 1.1.1

Artificial nanofabrication of integrated photonic circuits using deep learning

PreFab

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.

Prediction

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.

Correction

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.

Models

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
ANT	NanoSOI	ANF1 (May 6 2024)	d10 (Jun 8 2024)	ANT_NanoSOI_ANF1_d10
ANT	SiN	ANF1 (May 6 2024)	d1 (Jan 31 2024)	ANT_SiN_ANF1_d1
Generic	DUV-SOI	ANF1 (May 6 2024)	d0 (Jul 30 2024)	generic_DUV_SOI_ANF1_d0

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

Installation

Install PreFab via pip:

pip install prefab

Or clone the repository and install in development mode:

git clone https://github.com/PreFab-Photonics/PreFab.git
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

Guides

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!

License

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