Skip to main content

Artificial nanofabrication of integrated photonic circuits using deep learning

Project description

PreFab

PreFab logo

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.

Try Rosette: Want a more visual experience? Try Rosette - our new layout tool with PreFab models built in, designed for rapid chip design.

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.

Example of PreFab prediction

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.

Example of PreFab correction

Models

Each photonic nanofabrication process requires unique models, which are regularly updated with the latest data. The current models include (see the full list in 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

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

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 an 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.

prefab setup

Guides

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

Performance and Usage

PreFab models are hosted on a serverless cloud platform. Please keep in mind:

  • 🐢 Default CPU inference may be slower.
  • 🥶 The first prediction using optional GPU inference may take longer due to cold start server loading. Subsequent predictions will be faster.
  • 😊 Please be considerate of usage. Start with small tasks and limit usage during the initial stages. Thank you!

License

This project is licensed under the LGPL-2.1 license. © 2025 PreFab Photonics.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

prefab-1.3.0.tar.gz (11.9 MB view details)

Uploaded Source

Built Distribution

prefab-1.3.0-py3-none-any.whl (51.4 kB view details)

Uploaded Python 3

File details

Details for the file prefab-1.3.0.tar.gz.

File metadata

  • Download URL: prefab-1.3.0.tar.gz
  • Upload date:
  • Size: 11.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.23

File hashes

Hashes for prefab-1.3.0.tar.gz
Algorithm Hash digest
SHA256 c152f9a4906191d01fb1d83a9dcb5cde81d0b21c9d95b56df20ac642f4975a5e
MD5 87d9657c880cb03e630fa3e4ef2e449c
BLAKE2b-256 ebcb0a92019c0833ce3a4982bd371f8ea72f0a0a0216cc38a4caf3e0ea546260

See more details on using hashes here.

File details

Details for the file prefab-1.3.0-py3-none-any.whl.

File metadata

  • Download URL: prefab-1.3.0-py3-none-any.whl
  • Upload date:
  • Size: 51.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.23

File hashes

Hashes for prefab-1.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5cb9d384fba409949ccc6eda158cc69f284ac2f00a8cd9d9aa207073bb8b1a49
MD5 cd7bc5f3e89a1a4d3087ab88bb16a466
BLAKE2b-256 a523e09b86bcc3af58a9fdb1f8e22f3e9952c8ab7b0a705635c2caac8ce99e33

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page