RizzoLab/PIC-Flow-Dataset

PIC-Flow Dataset

22,500 frequency-domain FDTD electromagnetic-field simulations for parameterized silicon-on-insulator photonic devices at λ = 1.55 µm. Used as the training, validation, and test data for the PIC-Flow neural surrogate model.

Code, documentation, and inference notebooks live in the GitHub repo: Rizzo-Integrated-Photonic-Systems-Lab/PIC-Flow.

Contents

Path	Description
shards/shard_*.npz	Packed FDTD samples (~225 shards). Each shard contains many slots s0/, s1/, ...
shards/index.json	Manifest mapping (device, geometry_id, split, augment) → (shard, slot).
geometries.jsonl	One line per simulated device: family, geometric parameters, port count.

Per-sample fields

Each slot inside a shard carries:

Key	Shape	Description
eps	(160, 480) float32	Relative permittivity ε_r (Si core ≈ 5.8, SiO₂ cladding ≈ 2.09).
Ez_real, Ez_imag	(160, 480) float32	Real and imaginary parts of the complex E_z field, source-anchored phase.
src_mask	(160, 480) float32	Binary mask marking the active eigenmode-source port.
port_masks	(N_ports, 160, 480) float32	Per-port binary masks (e.g., 4 ports for MMIs/DCs, 3 for Y-branches).
port_ids	(N_ports,) int32	Integer port labels matching port_masks.
input_port	int	Which port was excited by the eigenmode source.
wavelength_um	float	Free-space wavelength in µm (1.55 throughout this dataset).
dx_um, dy_um, Lx_um, Ly_um	float	Grid resolution and physical extent.
device	string	Family: mmi, ybranch, or directional_coupler.
geometry_id, split	string	Unique geometry id and train / val / test membership.
params/<name>	float	Geometric parameters for this device (varies by family — see below).

Devices and parameter sweep

5-dimensional Latin-hypercube sweep per family, quantized to a half-pixel grid (0.025 µm at 20 pixels/µm):

• 2×2 MMI (4 ports, 7,500 samples): waveguide width [0.40–0.575], MMI width [4.5–5.5], MMI length [8.0–15.0], taper width [0.575–1.5], taper length [1.0–3.0] µm.
• Y-branch (3 ports, 7,500): waveguide width [0.40–0.575], junction length [1.0–3.0], bend length [4.0–7.0], arm offset [0.575–2.5], output length [1.0–4.0] µm.
• Directional coupler (4 ports, 7,500): waveguide width [0.40–0.575], gap [0.10–0.35], coupling length [5.0–8.0], bend length [4.0–6.0], port separation [0.825–2.0] µm.

Splits

Index-based, shared across all PIC-Flow ablation runs:

Split	Samples
train	18,000
val	2,250
test	2,250

Note on the Hugging Face dataset viewer. The viewer at the top of this page labels every shard as "train" because it auto-detects splits from filename patterns (train-*, test-*, validation-*). This dataset uses a different convention: splits are encoded per sample in shards/index.json (each entry has a split: "train" | "val" | "test" field), and a single .npz shard can contain samples from any of the three splits. The PIC-Flow dataloader (Model/dataset.py) reads index.json and partitions samples accordingly. The 18,000 / 2,250 / 2,250 split is what the dataloader actually serves — the viewer label is cosmetic.

How to download

pip install huggingface_hub
hf download RizzoLab/PIC-Flow-Dataset --repo-type dataset --local-dir Data/unified_sweep_mmi_ybranch_dc_7500_each_1p55um

The dataloader in the GitHub repo (Model/dataset.py) expects this layout under Data/unified_sweep_mmi_ybranch_dc_7500_each_1p55um/.

Generation

The data was produced with the Meep FDTD solver via FDTD/unified_sweep.py in the GitHub repo. Each sample uses an eigenmode source exciting the fundamental TE mode at the selected input port; fields are extracted at λ = 1.55 µm via discrete Fourier transforms. The vertical 220 nm SOI slab mode is pre-solved into an effective core index n_eff ≈ 2.4 used for the 2D scalar Helmholtz simulation.

To regenerate from scratch (~24 hours on one CPU node, 16 threads):

python FDTD/unified_sweep.py --output-dir Data/ \
    --devices mmi,ybranch,directional_coupler \
    --num-samples 7500 --wavelengths 1.55

Citation

@article{Quaratiello2026PICFlow,
  author  = {Joseph Quaratiello and Anthony Rizzo},
  title   = {Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices},
  journal = {arXiv},
  year    = {2026}
}

License

MIT.