Code for Fab-in-the-loop Reinforcement Learning
The optimization of silicon photonic components is a key challenge. The performance of simulated devices is significantly different than their measured performance due to the fabrication process. Fabrication effects such as sidewall angle and roughness are difficult to account for in conventional simulations; to do so, the simulation mesh must be made finer, resulting in dramatically increased simulation time. I came up with the idea of using machine learning to optimize these devices for a fabrication process. I call this approach fab-in-the-loop reinforcement learning. The key idea is to have the algorithm propose improved designs based on the measured results of previous fabrication runs. A new generation of improved devices based on these results is fabricated and measured and the best performing designs are found. The algorithm can be repeated until a device optimized to desired performance is generated.
If you find this useful, please cite our APL photonics paper “Reinforcement learning for photonic component design“ https://doi.org/10.1063/5.0159928
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