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 fabricated on an air clad 220 nm silicon on insulator single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm can cover a 150 nm bandwidth with less than 10.2 dB of loss at their lowest point.

Automated summary

In this paper, we propose a fab-in-the-loop reinforcement learning algorithm that takes into account the imperfections in nanofabrication processes for designing nano-photonic components. This algorithm significantly reduces the insertion loss and achieves low loss at the lowest point of the designed bandwidth.