Reinforcement Learning for Photonic Component Design

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers (PhCGC) fabricated on a 220nm silicon on insulator (SOI) single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 dB to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm are able to cover a 150nm bandwidth with less than 10.2 dB of loss at their lowest point.