Silicon Photonics with Artificial Neural Networks. SiPANN aims to implement various silicon photonics simulators based on machine learning techniques found in literature. The majority of these techniques are linear regression or neural networks. As a results SiPANN can return scattering parameters of (but not limited to)
Half Rings
Arbitrarily shaped directional couplers
Racetrack Resonators
Waveguides
And with the help of simphony and SiPANN’s accompanying simphony wrapper
Ring Resonators
Doubly Coupled Rings
Hybrid Devices (ie Green Machine)
Installation
SiPANN is distributed on PyPI and can be installed with pip:
pip install SiPANN
Developmental Build
If you want a developmental build, it can be had by executing
git clone https://github.com/contagon/SiPANN.git
pip install -e SiPANN/
This development version allows you to make changes to this code directly (or pull changes from GitHub) without having to reinstall SiPANN each time.
You should then be able to run the examples and tutorials in the examples folder, and call SiPANN from any other python file.
References
SiPANN is based on a variety of methods found in various papers, including:
[1] A. Hammond, E. Potokar, and R. Camacho, “Accelerating silicon photonic parameter extraction using artificial neural networks,” OSA Continuum 2, 1964-1973 (2019).
Bibtex citation
@misc{SiP-ANN_2019,
title={SiP-ANN},
author={Easton Potokar, Alec M. Hammond, Ryan M. Camacho},
year={2019},
publisher={GitHub},
howpublished={{https://github.com/contagon/SiP-ANN}}
}
Releasing
Make sure you have committed a changelog file titled “[major].[minor].[patch]-changelog.md” before bumping version.
To bump version prior to a release, run one of the following commands:
bumpversion major
bumpversion minor
bumpversion patch
This will automatically create a git tag in the repository with the corrresponding version number and commit the modified files (where version numbers were updated). Pushing the tags (a manual process) to the remote will automatically create a new release. Releases are automatically published to PyPI and GitHub when git tags matching the “v*” pattern are created (e.g. “v0.2.1”), as bumpversion does.
To view the tags on the local machine, run git tag. To push the tags to
the remote server, you can run git push origin <tagname>.
For code quality, please run isort and black before committing (note that the latest release of isort may not work through VSCode’s integrated terminal, and it’s safest to run it separately through another terminal).
Metadata
Release files for SiPANN 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| sipann-2.0.2.tar.gz | 761.8 kB | Without provenance | Details for sipann-2.0.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| sipann-2.0.2-py3-none-any.whl | Python 3 | none | any | Details for sipann-2.0.2-py3-none-any.whl |
Total release size: 1.5 MB
Release files / sipann-2.0.2.tar.gz
| Download URL | sipann-2.0.2.tar.gz |
|---|---|
| Size | 761.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Release files / sipann-2.0.2-py3-none-any.whl
| Download URL | sipann-2.0.2-py3-none-any.whl |
|---|---|
| Size | 759.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a5f6691520438ad27b7b79a43666a963295070bafb8688b581e495810cd317b9
|
|
BLAKE2b-256 checksum How to use checksums |
1b79cec9e4c1c84dee4fb258db5028e47915e1823fdd548f8514106fc8a8f60e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Security for SiPANN 2.0.2
Provenance
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No provenance found
None of the files in this release were uploaded with provenance metadata.
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