PreFab

PreFab logo

PreFab models fabrication process induced structural variations in integrated photonic devices using deep learning. New insights into the capabilities of nanofabrication processes are uncovered and device design fidelity is enhanced in this virtual nanofabrication environment.

Prediction

PreFab predicts process-induced structural variations such as corner rounding (both over and under etching), washing away of small lines and islands, and filling of narrow holes and channels in planar photonic devices. The designer then resimulates their (predicted) design to rapidly prototype the expected performance and make any necessary corrections prior to nanofabrication.

Example of PreFab prediction Predicted fabrication variation of a star structure on a silicon-on-insulator e-beam lithography process.

Correction

PreFab also makes automatic corrections to device designs so that the fabricated outcome is closer to the nominal design. Less structural variation generally means less performance degradation from simulation to experiment.

Example of PreFab correction Corrected fabrication of a star structure on a silicon-on-insulator e-beam lithography process.

Models

Each photonic foundry requires its own predictor and corrector models. These models are updated regularly based on data from recent fabrication runs. The following models are currently available:

Foundry	Process	Latest Version (Date)	Status	Name
ANT	NanoSOI	v2 (Jan 9 2023)	Beta	ANT_NanoSOI_v2

Installation

Local

Install the latest version of PreFab locally using:

pip install prefab

Performance and Usage

Currently, PreFab models are accessed through a serverless cloud platform that has the following limitations to keep in mind:

Inferencing is done on a CPU and will thus be relatively slow. GPU inferencing to come in future updates.
The first prediction may be slow, as the cloud server may have to perform a cold start and load the necessary model(s). After the first prediction, the server will be "hot" for some time and the subsequent predictions will be much quicker.
Please be mindful of your usage. Start with small examples before scaling up to larger device designs, and please keep usage down to a reasonable amount in these early stages. Thank you!

License

Metadata

Release files for prefab 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for prefab 0.2.0
File	Size	Uploaded
prefab-0.2.0.tar.gz	442.2 kB	Mar 1, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for prefab 0.2.0
File	Interpreter	ABI	Platform	Reset
prefab-0.2.0-py3-none-any.whl 25.7 kB Mar 1, 2023	Python 3	none	any	Details

Total release size: 467.9 kB

Release files / prefab-0.2.0.tar.gz

Download URL	prefab-0.2.0.tar.gz
Size	442.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`0307f215c037de9796e7c2793a5b01fccfbbe0ecbfcd4c0ac27780944fcce8e9`
BLAKE2b-256 checksum How to use checksums	`0b5a0a6f031910167cef92c7b9d7d834ce6940dbe749a9dae7864c75a5ab17e4`
Upload date	Mar 1, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.13`

Release files / prefab-0.2.0-py3-none-any.whl

Download URL	prefab-0.2.0-py3-none-any.whl
Size	25.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`ca6875fd575a31b0c45bb01b1e8e059fc4fd5f894bba146502f7d12387aacd3f`
BLAKE2b-256 checksum How to use checksums	`87735ccfe572937eb83e34ea1c1acab4d715a5d0d92cae8f5c4c2a539f80c894`
Upload date	Mar 1, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.13`