| --- |
| 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. |
| |
