Introduction

Silicon Photonics Toolkit

Various software packages and resources for rapid parameter lookup and calculation have been developed across a range of disciplines [1-17]. These resources offer easy and efficient access to standard parameters used in related areas of research. However, numerical simulators and equation solvers are often computationally expensive tools that require significant resources for simple data retrieval and calculations. To address this challenge, we present Silicon Photonics Toolkit that offers auto-differentiation and fast data retreival capabilities. The pre-saved data has been generated using Lumerical MODE Solutions’ FDE Solver and subsequently interpolated and mapped using a state-of-the-art Python package JAX. Data mapping function from JAX makes data lookup operations achieved in extremely fast running time, making it a valuable resource for researchers in the silicon photonics field. Silicon Photonics Toolkit provides a user-friendly and easy to use interface that enables rapid data access, leading to time-efficient research and accelerated progress in scientific discovery.

Getting Started

Silicon Photonics Toolkit is a toolkit providing fundamental waveguide and material properties to aid in the design of silicon photonic components on silicon-on-insulator platforms with high accuracy and extremely fast runtime. All the waveguide parameters returned by sipkit are calculated for 220-nm-thick strip waveguides on a SOI. See the documentation for tutorials and API reference.

Installation

Pip

The package can be installed via pip:

pip install sipkit

You can install siphotonics with additional packages for developers:

pip install sipkit[dev]

Build from source

Alternatively, the package can be built from source by cloning the repository and running the setup script:

git clone https://github.com/Photonic-Architecture-Laboratories/si-photonics-toolkit.git
cd si-photonics-toolkit
pip install -e .

Dependencies

The package requires the following packages to be installed:

NumPy
Jax

Citing SiPhotonics Toolkit

@software{silicon-photonics-toolkit2022github,
    url = {https://github.com/Photonic-Architecture-Laboratories/si-photonics-toolkit},
    author = {Aycan Deniz Vit and Kazım Görgülü and Ali Najjar Amiri and Emir Salih Mağden},
    title = {Silicon Photonics Toolkit},
    description = {A toolkit to rapidly lookup parameters for the design of silicon photonic components with automatic differentiation capability.},
    year = {2022},
}

References

1- Dunn, A., Wang, Q., Ganose, A., Dopp, D., Jain, A. Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm. npj Computational Materials 6, 138 (2020). https://doi.org/10.1038/s41524-020-00406-3

2- Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013). Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis (Version 2022.1.24) [Computer software]. https://doi.org/10.1016/j.commatsci.2012.10.028

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14- https://github.com/CitrineInformatics/MPEA_dataset

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16- https://pppdb.uchicago.edu/

17- https://oqmd.org/