PhotonFlow AI
Advanced optical component performance prediction powered by machine learning
AI-Powered
Physics-Informed
Real-Time
AI-Powered Predictions
Achieve ±0.1dB accuracy for insertion loss predictions using our advanced ML models
Physics-Informed
Combines machine learning with fundamental physics principles for reliable results
Real-Time Results
Get instant performance predictions without waiting for time-consuming simulations
PhotonFlow AI
Optical Performance Predictor
AI-Powered Predictions
Component Parameters
Prediction Results
Configure parameters and click "Predict Performance" to see results
Model Performance Metrics
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Usage Guidelines
Parameter Ranges
- • Width: 0.3 - 2.0 μm
- • Height: 0.15 - 0.34 μm
- • Wavelength: 1260 - 1650 nm
- • Temperature: -10 - 70 °C
Best Practices
- • Start with standard parameters for your technology platform
- • Consider temperature effects on performance
- • Check confidence scores for prediction reliability
- • Validate critical predictions with simulations
Supported Components
The current model version supports the following components with high accuracy:
- Strip Waveguide±0.1dB typical accuracy
- Directional Coupler±0.2dB typical accuracy
- Y-Splitter±0.15dB typical accuracy
- Ring Resonator±0.2dB typical accuracy
- MZ Modulator±0.25dB typical accuracy
- Grating Coupler±0.3dB typical accuracy