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license: apache-2.0
task_categories:
- image-segmentation
- image-to-image
language:
- en
tags:
- lithography
- semiconductor
- EUV
- OPC
- mask-optimization
- inverse-lithography
size_categories:
- n<1K
pretty_name: Litho-Tiny-100
configs:
- config_name: default
data_files:
- split: train
path: data/train-*.parquet
---
# Litho-Tiny-100
A tiny placeholder lithography dataset shipped by
[OpenLithoHub](https://github.com/OpenLithoHub/OpenLithoHub) — **100 deterministic
(mask, aerial-image) pairs** generated from the project's rule-based PDK-aware
synthesizer and Gaussian-PSF forward model.
> ⚠️ **This dataset is intentionally tiny.** It exists to nail down the schema
> and prove `load_dataset("OpenLithoHub/litho-tiny")` works end-to-end. If you
> need a real research-scale dataset, see the strategic plan's discussion of the
> upcoming `Litho-1M` pre-training set, or use one of the upstream sources
> referenced in OpenLithoHub's data layer.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("OpenLithoHub/litho-tiny", split="train")
print(ds)
print(ds.column_names)
# Decode the first mask
import io, numpy as np
from PIL import Image
mask = np.array(Image.open(io.BytesIO(ds[0]["mask"]))) # (512, 512) uint8 in {0, 255}
aerial = np.array(Image.open(io.BytesIO(ds[0]["aerial"]))) # (512, 512) uint8
# Or use the raw float32 bytes for full precision
mask_f32 = np.frombuffer(ds[0]["mask_npy"], dtype=np.float32).reshape(512, 512)
aerial_f32 = np.frombuffer(ds[0]["aerial_npy"], dtype=np.float32).reshape(512, 512)
```
## Schema
| Column | Type | Description |
|---------------|--------------|----------------------------------------------------------------|
| `id` | string | Stable identifier `<pattern>-<index>`, e.g. `sram-007`. |
| `pattern` | string | One of `sram`, `contact_array`, `random_logic`. |
| `pdk` | string | PDK preset (always `freepdk45` for this tiny version). |
| `size_px` | int32 | Mask edge length (512). |
| `seed` | int32 | PRNG seed used by the synthesizer. |
| `mask` | bytes (PNG) | Binary mask, 8-bit grayscale, `{0, 255}`. |
| `aerial` | bytes (PNG) | Aerial-image intensity normalized to `[0, 255]` (clipped at 1).|
| `mask_npy` | bytes | Raw `float32` mask, row-major `(H, W)`. |
| `aerial_npy` | bytes | Raw `float32` aerial intensity (un-clipped), row-major `(H, W)`. |
Counts: 34 SRAM + 33 contact-array + 33 random-logic = **100 rows**, all in `train`.
## Reproducing
```bash
git clone https://github.com/OpenLithoHub/OpenLithoHub.git
cd OpenLithoHub
pip install -e '.[data]'
python scripts/build_litho_tiny.py --out out/litho-tiny
```
The script is fully deterministic; identical commits produce identical bytes.
## Forward model
The aerial images come from `openlithohub._utils.forward_model.simulate_aerial_image`
— a Gaussian-PSF approximation (`sigma_px=4.0`, `dose=1.0`) of the Hopkins forward
model, with circular padding. For a research-grade SOCS Hopkins simulation, see
`openlithohub._utils.hopkins.simulate_aerial_image_hopkins`.
## License
Apache-2.0 — same as OpenLithoHub itself. Patterns are synthetic and free of any
real fab IP.
## Citation
If this dataset helped your work, please ⭐ the
[OpenLithoHub repo](https://github.com/OpenLithoHub/OpenLithoHub) and cite the project.
|