Architecture¶

OpenLithoHub uses a layered architecture designed for extensibility and separation of concerns.

Layer Overview¶

Layer	Module	Responsibility
Data	`openlithohub.data`	Dataset loading, format conversion, resolution alignment
Benchmark	`openlithohub.benchmark`	Metrics computation, compliance checking, reporting
Models	`openlithohub.models`	Abstract model interface, registry, example implementations
Workflow	`openlithohub.workflow`	Layout parsing, tiling, contour extraction, OASIS export
CLI	`openlithohub.cli`	User-facing commands via Typer
Leaderboard	`openlithohub.leaderboard`	SOTA tracking, submission, querying

Data Flow¶

Dataset (LithoBench/LithoSim)
        │
        ▼
┌─────────────────┐
│  DataAdapter    │  Normalize to (B, C, H, W) tensors
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ LithographyModel│  Predict optimized mask
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Benchmark     │  Compute EPE, PV Band, MRC/DRC
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Workflow      │  Tile → Contour → B-spline → OASIS
└────────┬────────┘
         │
         ▼
   Fab-ready mask (.oas)

Data Layer¶

The data layer provides unified access to lithography datasets through the DatasetAdapter interface:

LithoBenchAdapter — loads NumPy .npy files from LithoBench (NeurIPS'23)
LithoSimAdapter — loads HuggingFace Parquet datasets from LithoSim (NeurIPS'25)

All adapters produce (design, target) tensor pairs with consistent shape (B, 1, H, W).

Benchmark Layer¶

Metrics¶

Metric	Function	Description
EPE	`compute_epe()`	Edge Placement Error between predicted and target contours
PV Band	`compute_pvband()`	Process variation band width across dose/focus window
Shot Count	`compute_shot_count()`	Mask write time proxy for MBMW/VSB writers
Stochastic	`compute_stochastic_robustness()`	Monte Carlo photon noise bridge/break probability

Compliance¶

Check	Function	Description
MRC	`check_mrc()`	Minimum width/spacing rule check (hard-fail)
DRC	`check_drc()`	Full design rule check: area, notch, width, spacing

Model Layer¶

Models implement the LithographyModel abstract class:

class LithographyModel(ABC):
    @property
    @abstractmethod
    def name(self) -> str: ...

    @property
    @abstractmethod
    def supports_curvilinear(self) -> bool: ...

    @abstractmethod
    def predict(self, design: Tensor, **kwargs) -> PredictionResult: ...

The ModelRegistry provides decorator-based registration and lookup by name.

Workflow Layer¶

The workflow layer converts tensor masks to fab-ready OASIS files:

Tiling — split large layouts into manageable tiles with configurable overlap
Contour Extraction — convert binary masks to polygon boundaries (manhattan or curvilinear)
B-spline Fitting — smooth curvilinear contours with configurable control point density
OASIS Export — write polygons to industry-standard OASIS format

Leaderboard¶

The leaderboard system uses a JSON-backed store with Pydantic validation:

Schema — BenchmarkResult model with typed fields for all metrics
Tracker — file-backed store with atomic writes and query filtering
CLI integration — openlithohub leaderboard view/submit/export commands