• Grating Coupler: a Tidy3D simulation setup
  • Initial Grating Design
  • Defining Our Initial Guess: A Uniform Grating
  • Running the Simulation
  • Visualizing the Results
  • Baseline Performance: The Need for Optimization
• Grating Coupler: Bayesian Optimization for Initial Design
  • Why Bayesian Optimization?
  • The Evaluation Function
  • Setting Up the Bayesian Optimizer
  • Framing the Problem: A 5-Parameter Global Search
  • Running the Optimization
  • Analyzing the Results
  • Interpreting the Optimization Progress
  • Visualizing the Optimized Design
  • Exporting the Best Design
• Adjoint Optimization: High-Dimensional Gradient-Based Refinement
  • The Power of the Adjoint Method
  • High-Dimensional Parameterization
  • Running the Gradient Descent
  • Visualizing the Results
  • Performance Payoff
  • Final Apodized Geometry
  • Conclusion and Next Steps
• Fabrication Sensitivity Analysis: Is Our Design Robust?
  • Modeling Fabrication Errors with a Bias
  • Interpreting the Sensitivity Plot
  • Monte Carlo
  • Adjoint
    • Linearized Sensitivity via Adjoint
    • Interpreting Variance Contributions
  • Comparison
    • Monte Carlo vs. Adjoint View
  • Analysis and Conclusion
  • Next Step: Designing for Robustness
• Robust adjoint optimization for manufacturability
  • Defining a Robust Multi-Objective Function
    • Starting Point and Bounds
  • Running the Robust Optimization
    • Tracking Progress
    • Pre- and Post-Optimization Bias Sweeps
  • The Final Payoff: Visualizing Robustness
    • Exporting the Robust Design
• Monte Carlo View: Nominal vs Robust Grating
  • Shared Monte Carlo Draws
  • Distribution of Center-Wavelength Loss
  • What the numbers say
• Measurement Calibration: Bridging Simulation and Fabrication
  • Calibration Workflow Overview
  • Generating Reference and Synthetic Measurement Data
  • Calibration Objective
  • Takeaways