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README.md

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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.

data/train-00000-of-00001.parquet

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+size 14641964