# FDTDX

FDTDX is an efficient open-source Python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Built on JAX, it provides native GPU support and automatic differentiation capabilities, making it ideal for large-scale 3D design in nanophotonics.

## Key Features

- **High Performance**: GPU-accelerated FDTD simulations with multi-GPU scaling capabilities
- **Memory Efficient**: Leverages time-reversibility in Maxwell's equations for efficient gradient computation
- **Automatic Differentiation**: Built-in gradient-based optimization for complex 3D structures
- **User-Friendly API**: Intuitive positioning and sizing of objects in absolute or relative coordinates
- **Large-Scale Design**: Capable of handling simulations with billions of grid cells
- **Open Source**: Freely available for research and development

## Documentation

Visit our [documentation](https://ymahlau.github.io/fdtdx) for:
- Detailed API reference
- Tutorial guides
- Example simulations
- Best practices


## Installation

Install FDTDX using pip:

```bash
pip install fdtdx
```

For development installation, clone the repository and install in editable mode:

```bash
git clone https://github.com/ymahlau/fdtdx
cd fdtdx
pip install -e .
```

## Multi-GPU

```bash
# The following lines often lead to better memory usage in JAX
# when using multiple GPU.
export XLA_PYTHON_CLIENT_ALLOCATOR=platform
export XLA_PYTHON_CLIENT_PREALLOCATE=false
export NCCL_LL128_BUFFSIZE="-2"
export NCCL_LL_BUFFSIZE="-2"
export NCCL_PROTO="SIMPLE,LL,LL128"
```

## Citation
If you find this repository helpful for you work, please consider citing:
```
@article{schubert2024quantized,
  title={Quantized Inverse Design for Photonic Integrated Circuits},
  author={Schubert, Frederik and Mahlau, Yannik  and Bethmann, Konrad and Hartmann, Fabian and Caspary, Reinhard and Munderloh, Marco and Ostermann, J{\"o}rn and Rosenhahn, Bodo},
  journal={arXiv preprint arXiv:2407.10273},
  year={2024}
}
```
