Transformer-based learned dimensional collapse for silicon photonic inverse design
2026 Conference on Physics and Simulation of Optoelectronic Devices, San-Francisco, Costa Rica, 19 - 22 January 2026, vol.13890, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 13890
- Doi Number: 10.1117/12.3080973
- City: San-Francisco
- Country: Costa Rica
- Middle East Technical University Affiliated: Yes
Abstract
We present a deep learning framework that revolutionizes photonic device design by collapsing costly 3D electromagnetic simulations into fast, accurate 2D representations. Our dual-stage, Transformer-based architecture combines a rapid factorization-cached 2D FDFD solver with a U-Transformer network to reconstruct full 3D fields, while a dedicated phase module preserves phase integrity. Trained on 16,000 3D-FDTD simulations of random silicon photonic devices, our model achieves over 99.1% field-matching accuracy and enables inverse design optimizations that are over 100 times faster than traditional methods. Designed devices exhibit less than 0.5 dB transmission mismatch and more than 90% structural similarity to 3D-FDTD results across the 1.5-1.6 mu m wavelength range. This scalable approach enables high-throughput, rapid, and practical design workflows for next-generation photonic components.