Datasets:

OpenLithoHub
/

litho-tiny

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 — 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

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

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 and cite the project.