Quickstart

Once you have completed installation (see Getting Started -> Installation), you are ready to run ORCA.

1. Prepare Inputs

You need:

• A geometry class (BaseGeometry subclass) with parameter definitions.
• A stackup XML file describing the physical layer stack.
• A Palace simulation configuration file (.simcfg).
• A Palace executable (local or via Apptainer container).

For an immediate starting point, use the built-in TransformerOcta geometry from examples/.

2. Run GUI Mode

orca

In GUI mode:

1. Select a geometry class.
2. Configure pipeline stages and parameters.
3. Set Palace executable path.
4. Start the pipeline.

3. Run Script Mode

For direct integration into scripts or custom workflows:

import orca
from orca import ORCA
from orca.geometry.examples.transformer.tf_octa_c_ports import TransformerOcta

geometry = TransformerOcta()

orca_instance = ORCA(
    [
        orca.GDSGenerator(num_samples=1000),
        orca.GDSConverter(),
        orca.PalaceSimulator(
            palace_executable="apptainer exec ~/palace/palace.sif palace",
            touchstone_type="dc_deembedded",  # "all", "normal", "deembedded", "dc", "dc_deembedded"
        ),
        orca.ModelTrainer(
            # hyperparameters=None,   # If None, Optuna tunes automatically
            # test_frac=0.15,         # Fraction of data held out for testing
            # n_train_samples=None,   # Optional cap on training samples
            # n_fold_cv=5,            # Cross-validation folds during tuning
        ),
        orca.OnnxExporter(),
        orca.ModelTester(),
    ]
)

orca_instance.run(geometry=geometry, cpu_cores=16)

This will:

1. Generate 1000 parameterised GDS layout variants.
2. Convert each to a Palace mesh and run full-wave EM simulation.
3. Store results in Touchstone format under output/<geometry_name>/.
4. Train a neural network on the simulation dataset.
5. Export the trained model to ONNX format.
6. Test prediction accuracy against held-out simulation data.

Tip

You can run only a subset of pipeline stages by modifying the list passed to ORCA(...). Stages are sorted by their internal index and some depend on outputs of earlier stages. Stage indices: GDSGenerator=0, GDSConverter=1, PalaceSimulator=2, ModelTrainer=4, OnnxExporter=5, ModelTester=6.

4. Run at Scale with OpenStack

For large-scale simulation campaigns, we provide an OpenStack VM image and a REST API with corresponding client CLI. See the ORCA-OpenStack repository for details.

5. Inspect Results

Each run stores artifacts under output/<geometry_name>/:

output/<geometry_name>/
    context.json
    geometries/
    models/
    palace_sims/
    results/

Typical contents include:

• Touchstone .sNp files per simulated sample.
• PyTorch model checkpoint.
• Exported .onnx surrogate model.
• Full run context JSON.

Tip

The exported .onnx file can be used directly with COBRA for circuit-level RFIC optimization.