prefab 1.4.2

Artificial nanofabrication of integrated photonic circuits using deep learning

PreFab

PreFab is a virtual nanofabrication environment that leverages deep learning and computer vision to predict and correct for structural variations in integrated photonic devices during nanofabrication.

Prediction

PreFab predicts process-induced structural variations, including corner rounding, loss of small lines and islands, filling of narrow holes and channels, sidewall angle deviations, and stochastic effects. This allows designers to rapidly prototype and evaluate expected performance pre-fabrication.

Correction

PreFab corrects device designs to ensure that the fabricated outcome closely matches the intended specifications. This minimizes structural variations and reduces performance discrepancies between simulations and actual experiments.

Installation

Install PreFab using pip:

pip install prefab

For contributors who wish to make changes to the source code:

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and set up the project
git clone https://github.com/PreFab-Photonics/PreFab.git
cd PreFab
uv sync

Getting Started

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

To link your account, run the following command to authenticate your token:

prefab setup

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

License

LGPL-2.1 © PreFab Photonics