Simulate Any Photonic Component in Seconds
Physics-informed neural networks trained on millions of simulations. Get accurate results 1000x faster than traditional solvers.
0.1s
Average prediction time
98.5%
Accuracy vs FDTD
Live Demo
1000x Faster Than Traditional Methods
Traditional FDTD
2-6 hours
Per simulation
- • High computational cost
- • Limited design exploration
- • Requires HPC resources
Mode Solvers
5-30 min
Limited to 2D
- • Only eigenmode analysis
- • No full 3D structures
- • Still slow for optimization
FluxCore AI
0.1-1s
Instant results
- • Real-time optimization
- • Full 3D simulations
- • Runs on any device
Comprehensive Component Coverage
Pre-trained models for all major photonic components across multiple material platforms
Strip Waveguides
Rib Waveguides
Slot Waveguides
Directional Couplers
MMI Couplers
Y-Branch Splitters
Ring Resonators
Disk Resonators
Bragg Gratings
AWG Filters
Echelle Gratings
Edge Couplers
Don't see your component? We add new models weekly.
Integrate AI Predictions Into Your Workflow
Simple REST API, Python SDK, and MATLAB toolbox for seamless integration
Python SDK
import fluxcore
# Initialize client
client = fluxcore.Client(api_key="your_api_key")
# Define waveguide parameters
waveguide = fluxcore.Waveguide(
material="silicon",
width=500, # nm
height=220, # nm
wavelength=1550 # nm
)
# Run prediction
result = client.predict(waveguide)
# Access results
print(f"Effective index: {result.n_eff}")
print(f"Loss: {result.loss} dB/cm")
print(f"Group index: {result.n_g}")
print(f"Dispersion: {result.dispersion} ps/nm/km")
# Batch predictions for optimization
widths = range(400, 600, 10)
results = client.batch_predict([
fluxcore.Waveguide(width=w, height=220, wavelength=1550)
for w in widths
])
# Find optimal width for target n_eff
optimal = min(results, key=lambda r: abs(r.n_eff - 2.4))
print(f"Optimal width: {optimal.width}nm")Real-World Impact
See how companies are accelerating photonic design with AI predictions
QuantumCore Systems
Challenge
6-week design cycles for quantum photonic processors
Solution
Reduced to 3 days with 98% accuracy predictions
Impact
Launched product 4 months ahead of schedule
FiberLink Networks
Challenge
Complex 400G transceiver optimization
Solution
AI-driven design space exploration in hours
Impact
30% power reduction, 95% yield
AutoSense Tech
Challenge
Slow iteration on beam steering circuits
Solution
Real-time optimization of OPA designs
Impact
10x faster time-to-market
Physics-Informed Neural Networks
Our AI models don't just pattern match - they understand the underlying physics. Trained on millions of FDTD simulations and validated against fabricated devices.
Physics Constraints
Built-in Maxwell's equations ensure physically accurate results
Massive Training Data
10M+ simulations across parameter space for robust predictions
Continuous Learning
Models improve with every fabrication run and measurement
Neural Architecture
Input Layer
Geometry + Material
Hidden Layers
Physics Encoding
Output Layer
S-Parameters
Validation
98.5% Accuracy