Fabrication-aware inverse design ================================ .. meta:: :description: Fabrication-aware inverse design seminar notebooks and walkthrough :keywords: tidy3d, inverse design, fabrication-aware, adjoint, grating coupler The October 9, 2025 seminar walks through a complete dual-layer grating coupler workflow: start from a uniform baseline, pull a strong seed design with Bayesian optimization, switch to adjoint gradients for per-tooth control, study fabrication sensitivities, and close the loop with measurement-driven calibration. Everything runs inside Tidy3D, so you can rerun the exact same jobs or adapt the utilities to your own device stack. Seminar recording: `YouTube link `_ Notebook lineup ---------------- * :doc:`Setup Guide: Building the Simulation <../notebooks/2025-10-09-invdes-seminar/00_setup_guide>` - builds the nominal SiN stack, launches the reference simulation, and visualizes the initial geometry so the later notebooks can reuse the cached job ID. * :doc:`Bayesian Optimization: Finding a Strong Baseline <../notebooks/2025-10-09-invdes-seminar/01_bayes>` - uses a five-parameter Bayesian search to quickly find a good uniform grating. This provides a practical baseline before investing in gradients. * :doc:`Adjoint Optimization: High-Dimensional Refinement <../notebooks/2025-10-09-invdes-seminar/02_adjoint>` - expands to per-tooth parameters and applies Adam with adjoint sensitivities to apodize the grating and boost efficiency. * :doc:`Fabrication Sensitivity Analysis: Is Our Design Robust? <../notebooks/2025-10-09-invdes-seminar/03_sensitivity>` - sweeps :math:`\pm 20` nm etch bias, runs Monte Carlo samples, and logs adjoint-derived sensitivity units (:math:`\Delta` objective / :math:`\Delta` parameter) so readers understand what the gradients mean physically. * :doc:`Robust Adjoint Optimization for Manufacturability <../notebooks/2025-10-09-invdes-seminar/04_adjoint_robust>` - penalizes variance across nominal/over/under corners, illustrating a fabrication-aware adjoint loop that matches what we demoed live. * :doc:`Monte Carlo View: Nominal vs Robust Grating <../notebooks/2025-10-09-invdes-seminar/05_robust_comparison>` - reruns the Monte Carlo campaign for both nominal and robust devices to quantify yield improvements. * :doc:`Measurement Calibration: Bridging Simulation and Fabrication <../notebooks/2025-10-09-invdes-seminar/06_measurement_calibration>` - demonstrates gradient-based calibration of tooth widths against (synthetic) spectra, using adjoint sensitivities to recover the as-fabricated geometry from optical measurements. Getting the code ---------------- The notebooks are available in the `Tidy3D notebooks repository `_. You will need the ``.ipynb`` files as well as the helper scripts `setup.py `_ and `optim.py `_ to run the examples. How to run the series --------------------- 1. Install ``tidy3d`` and ``bayesian-optimization`` (``pip install tidy3d bayesian-optimization``) and configure your API key. 2. Execute the notebooks in order; each step writes results into ``results/`` and later notebooks assume those JSON files exist. Supporting assets ----------------- * `setup.py `_ - shared simulation builders, fabrication constraints, and helper functions. * `optim.py `_ - a lightweight, autograd-friendly Adam implementation with parameter clipping. * ``results/`` - JSON checkpoints (Bayes best point, adjoint refinements, robust design) consumed by subsequent notebooks.