SPINS Photonics

HyperWave

Inverse Design for Next-Generation Photonics.

Industrial-grade 3D FDTD built for efficient, manufacturable device optimization.

Inverse Design in Action

Watch gradient-based optimization converge to high-efficiency photonic devices in real time.

api_workflow.py
pip install hyperwave-community

import hyperwave_community as hwc
hwc.configure_api(api_key="your-api-key")

# Build device from GDSFactory component
recipe = hwc.build_recipe(
    component_name="mmi2x2_with_sbend",
    resolution_nm=20,
    n_core=3.48,
)

# Build monitors and solve waveguide mode
monitors = hwc.build_monitors(recipe, source_port="o1")
source = hwc.solve_mode_source(recipe, monitors)

# Run on B200 GPU
results = hwc.run_simulation(recipe, monitors, source)

# Analyze transmission
hwc.analyze_transmission(results)

Developer-Friendly. Enterprise-Ready.

A clean, Pythonic API built for photonics engineers. Define your simulation, run optimization, and analyze results, directly from a notebook or production environment.

  • GPU-Accelerated Backend

    Optimized 3D FDTD powered by NVIDIA B200 GPUs for high-throughput simulation and optimization.

  • Integrated Adjoint Differentiation

    Compute gradients directly within the solver for efficient inverse design.

  • Jupyter-Native Workflow

    Inline visualization with seamless integration into Jupyter and Colab notebooks.

  • GDSII Import and Export

    Build from GDSFactory components and export fabrication-ready designs.

Simple, Transparent Pricing

Usage-based pricing for individuals. Custom infrastructure options for enterprise teams.

Individual

$25/hour of GPU simulation
No upfront software licenses
No subscriptions or contracts
Credits never expire

Enterprise

Custom Pricing

Dedicated engineering support
Priority feature collaboration
Flexible deployment options

Why HyperWave

Built for photonics engineers who demand speed, control, and practical workflows.

Accelerate Design Cycles

GPU-accelerated 3D FDTD delivers simulation results in minutes, enabling rapid iteration compared to traditional CPU-based workflows.

Purpose-Built for Inverse Design

Automatic differentiation enables efficient gradient-based topology optimization that converges to fabrication-ready designs.

Python-First Developer Experience

A clean Python SDK with Jupyter and Colab support, letting you offload compute to cloud GPUs, integrate with GDSII, and manage your full simulation workflow programmatically.

Ready to accelerate your photonics design?

Get started and run your first GPU-accelerated simulation today.