solwa 0.1.0

Differentiable RCWA in PyTorch, based on TORCWA

solwa-0.1.0

• License: LGPL

Differentiable Rigorous Coupled-Wave Analysis (RCWA) package based on PyTorch

Features

solwa (solar + rcwa) is a PyTorch implementation of rigorous coupled-wave analysis (RCWA) based on torcwa https://github.com/kch3782/torcwa

• GPU-accelerated simulation

• Supporting automatic differentiation for optimization

• Units: Lorentz-Heaviside units

  • Speed of light: 1

  • Permittivity and permeability of vacuum: both 1

• Notation: exp(-jωt)

Installation

currently only installation from source is supported

PyTorch ≥ 2.10 is recommended for improved CUDA performance (see PR: https://github.com/pytorch/pytorch/pull/166715)

significant changes from the original torcwa

1. Constants are loaded from standard libraries instead of defining them again in the code.
2. Major cleanup of the code structure for better readability and maintainability.
3. Better error handling and messages.
4. Addition of functions for calculation of the poynting vector and power flux.

solwa Examples

1. Example 0: Fresnel equation

2. Example 1: Simulation with rectangular meta-atom
   Normal incidence / Parametric sweep on wavelength / View electromagnetic field

3. Example 1-1: Simulation with stacked meta-atom
   Normal incidence / View electromagnetic field

4. Example 2: Simulation with square meta-atom
   Oblique incidence / View electromagnetic field

5. Example 3: Simulation with rectangular meta-atom
   Normal incidence / Parametric sweep on geometric parameters

6. Example 4: Gradient calculation of cylindrical meta-atom
   Differentiation of transmittance with respect to radius

7. Example 5: Shape optimization
   Maximize anisotropy

8. Example 6: Topology optimization
   Maximize 1st order diffraction

Acknowledgements

The fundamental implementation, including mathematical formulation, was implemented in the torcwa package by Changhyun Kim and Byoungho Lee.

This solwa package is a modified version of torcwa for better usability. It will be continuously updated to include more features and improvements.

If this package is useful for your research, please cite the following paper:

@article{
	title = {TORCWA: GPU-accelerated Fourier modal method and gradient-based optimization for metasurface design},
	journal = {Computer Physics Communications},
	volume = {282},
	pages = {108552},
	year = {2023},
	doi = {https://doi.org/10.1016/j.cpc.2022.108552},
	author = {Changhyun Kim and Byoungho Lee},
}

Work on the solwa package is supported by the ERC Consolidator Grant (No. 101125948, PHASE).

Furthermore, the authors acknowledge support by the state of Baden-Württemberg through bwHPC through providing computational resources.

The work on the original torcwa was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2B5B02002730) and Samsung Electronics Co., Ltd (IO201214-08164-01).