OpenLithoHub

Open-source computational lithography benchmarking and workflow toolkit for advanced EUV/curvilinear mask processes.

Website: openlithohub.com | Docs: docs.openlithohub.com | Playground: HuggingFace Space

中文版 / Chinese Version

What is OpenLithoHub?

OpenLithoHub provides a unified evaluation and workflow framework for computational lithography research. It bridges the gap between academic tensor-based optimization and industrial mask manufacturing by offering:

• Unified dataset access — single interface to LithoBench, LithoSim, GAN-OPC, ICCAD'16 hotspot, ASAP7, FreePDK45 + NanGate OCL, and ORFS-routed RISC-V layouts
• Standardized metrics — EPE, PV Band, shot count, EUV stochastic robustness, hotspot detection (recall / precision / F1)
• Manufacturing compliance — MRC/DRC rule checking as hard-fail gating
• OASIS workflow — end-to-end pipeline from tensor to fab-ready mask (manhattan & curvilinear)
• Model-agnostic evaluation — plug any OPC/ILT model into the benchmark via a minimal interface

┌─────────────────────────────────────────────────────────────────┐
│                       OpenLithoHub                              │
├─────────────┬──────────────┬──────────────┬───────────┬─────────┤
│  Data Layer │  Benchmark   │   Workflow   │ Vis & UX  │   CLI   │
│ LithoBench  │  EPE/PVBand  │ Tiling/Stitch│ Paper figs│ eval    │
│ LithoSim    │  MRC/DRC     │ Contour Ext. │ Jupyter   │ optimize│
│ Transforms  │  Stochastic  │ OASIS Export │ EDA bridge│leaderbd │
│ Dummy gen.  │  Shot Count  │ B-spline Fit │           │         │
└─────────────┴──────────────┴──────────────┴───────────┴─────────┘

Installation

# Core (metrics + CLI)
pip install openlithohub

# With dataset support (HuggingFace, parquet)
pip install openlithohub[data]

# With full workflow (KLayout, scipy for B-spline)
pip install openlithohub[workflow]

# Everything
pip install openlithohub[all]

From source (development):

git clone https://github.com/OpenLithoHub/OpenLithoHub.git
cd OpenLithoHub
pip install -e ".[dev]"

Quick Start

Evaluate a model

openlithohub eval run \
  --model dummy-identity \
  --dataset lithobench \
  --data-root ./data/lithobench \
  --format table

Output:

┌──────────────────┬────────────────┐
│ Metric           │ Value          │
├──────────────────┼────────────────┤
│ epe_mean_nm      │ 0.0000         │
│ epe_max_nm       │ 0.0000         │
│ mrc_violation_rate│ 0.0000        │
│ mrc_passed       │ 1.0000         │
└──────────────────┴────────────────┘

Run end-to-end optimization

openlithohub optimize run \
  --input design.oas \
  --model your-model \
  --writer mbmw \
  --node 3nm-euv \
  --drc-check \
  --output optimized.oas

Use as a Python library

import torch
from openlithohub.benchmark.metrics import compute_epe, compute_pvband
from openlithohub.benchmark.compliance import check_mrc, check_drc

predicted = torch.load("predicted_mask.pt")
target = torch.load("target_mask.pt")

# Edge Placement Error
epe = compute_epe(predicted, target, pixel_size_nm=1.0)
print(f"EPE mean: {epe['epe_mean_nm']:.2f} nm")

# Process Variation Band
pvb = compute_pvband(predicted, defocus_range_nm=20.0)
print(f"PV Band: {pvb['pvband_mean_nm']:.2f} nm")

# Manufacturing compliance
mrc = check_mrc(predicted, min_width_nm=40.0, min_spacing_nm=40.0)
print(f"MRC passed: {mrc.passed} ({mrc.violation_count} violations)")

Register a custom model

from openlithohub.models.base import LithographyModel, PredictionResult
from openlithohub.models.registry import registry

@registry.register
class MyOPCModel(LithographyModel):
    @property
    def name(self) -> str:
        return "my-opc"

    @property
    def supports_curvilinear(self) -> bool:
        return True

    def predict(self, design: torch.Tensor, **kwargs) -> PredictionResult:
        mask = my_optimization_algorithm(design)
        return PredictionResult(mask=mask)

Paper-ready figures

from openlithohub.vis import plot_contours

# Vector PDF, IEEE column-width, colorblind-safe palette
plot_contours(target, predicted, save_path="fig.pdf", style="ieee")

Hermetic dummy layouts (for CI / Colab)

from openlithohub.data import generate_dummy_layout

mask = generate_dummy_layout(size=256, seed=0)  # numpy + torch only, no KLayout

EDA bridge (Calibre / IC Validator)

from openlithohub.workflow import BridgeRules, emit_bridge_bundle

emit_bridge_bundle(
    "optimized.oas",
    BridgeRules(min_width_nm=40.0, min_spacing_nm=40.0),
)
# Writes optimized.svrf, optimized.rs, optimized.bridge.md

Try it in Colab

The notebooks/quickstart.ipynb tutorial runs end-to-end on Colab's stock runtime — install, generate a layout, score it, and produce a paper-ready figure in three minutes.

For plugging your own model into the harness, use the BYOM tutorial — it walks through subclassing LithographyModel, running the standard metric suite, and formatting a leaderboard submission.

Architecture

Metrics

Supported Datasets

Baselines

Reference numbers for the bundled models on eight synthetic 64×64 layouts (square, line, line/space, T, L, cross, contacts, dense lines). These are generated end-to-end by scripts/generate_baselines.py and persisted under baselines/. See docs/benchmarks.md for the methodology, the Hopkins forward model, and reproduction instructions.

See baselines/results.md for per-pattern breakdowns.

Reproduce locally:

python scripts/generate_baselines.py --synthetic --limit 8 --output baselines/

Optical forward models

OpenLithoHub ships two differentiable forward models, both written in pure PyTorch so the entire ILT loop is end-to-end auto-differentiable:

Switch LevelSetILTModel to Hopkins:

from openlithohub._utils import HopkinsParams
from openlithohub.models.levelset_ilt import LevelSetILTModel

model = LevelSetILTModel(
    iterations=200,
    forward_model="hopkins",
    hopkins_params=HopkinsParams(
        wavelength_nm=193.0, na=1.35, sigma=0.7, num_kernels=24, pixel_size_nm=2.0,
    ),
)

Development

# Run tests
pytest tests/ -v

# Lint
ruff check src/ tests/

# Type check
mypy src/

# Format
ruff format src/ tests/

Roadmap

• Milestone 1: Unified data adapters, EPE metric, eval CLI
• Milestone 2: MRC compliance, Manhattan contour extraction, tiling, shot count
• Milestone 3: OASIS workflow, PV Band, stochastic robustness, DRC, B-spline fitting, optimize CLI
• Milestone 4: Public leaderboard, MkDocs documentation site, CI/CD for docs
• Milestone 5: Web playground (HuggingFace Spaces)
• Milestone 6: Real ILT models (LevelSet-ILT, Neural-ILT U-Net), DTCO process nodes, resist simulation, model hub, Jupyter integration, PyPI/Docker CI/CD
• Milestone 7: Paper-ready visualization, dummy layout generator, EDA bridge templates, Colab quickstart
• Milestone 8: Multi-stage KLayout Docker, AI-engineer terminology guide, Auto-Leaderboard CI, community charter (Discord), v0.1 launch announcement
• Milestone 9: PDK-aware synthetic layout generator, vendor-neutral simulator hook API, EUV 3D-mask shadow proxy, Monte Carlo failure metric, Mini-Hackathon (2026-Q3), RFC 0001 (Layout-MAE) + RFC 0002 (Layout Tokens)
• Milestone 10: Real PDK rollout — ASAP7 standard cells, FreePDK45 + NanGate OCL, ORFS-routed RISC-V mock-alu (issue #4)

Related Projects

Contributing

See CONTRIBUTING.md for guidelines.

Community

A Discord server for OpenLithoHub is launching 2026-Q3 — channels for model discussion, physics simulation, help, and showcase. The place to debate model design, reproducibility, and benchmarks.

Want to be notified when the invite goes live? Open an issue with the community label or watch this repo. Charter, channel structure, etiquette, and onboarding flow are documented in docs/community.md.

📣 Read the launch announcement: v0.1 release post — includes paste-ready copy for X / LinkedIn / 知乎 / HuggingFace Forum.

🏆 Mini-hackathon launching 2026-Q3 — charter & rules. EPE target, frozen test split, hard MRC/DRC gate, separate leaderboard track.

Disclaimer

OpenLithoHub is a purely academic, open-source project for fundamental research in computational physics and machine learning. It relies solely on publicly available datasets and published algorithms. It does not contain, nor does it seek to reverse-engineer, any proprietary commercial EDA tools or export-controlled manufacturing processes.

License

OpenLithoHub uses a layered licensing model:

• Code — Apache License 2.0
• Documentation — CC-BY-SA 4.0
• Datasets — each dataset retains its original license; OpenLithoHub ships only adapters, not data. See DATA-LICENSES.md.
• Third-party components — see NOTICE.

You may freely use OpenLithoHub commercially under the open-source license (attribution and the NOTICE file are the only requirements). For commercial licensing options without attribution, with patent indemnification, or with SLA-backed support, see COMMERCIAL-USE.md.

To cite OpenLithoHub in academic work, see CITATION.cff. Contributors: please review CONTRIBUTING.md and the Contributor License Agreement. Security issues: SECURITY.md.