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

Cryogenic Optical Packaging Using Photonic Wire Bonds

We present the required techniques for the successful low loss packaging of integrated photonic devices capable of operating down to 970 mK utilizing photonic wire bonds. This scalable technique is shown to have an insertion loss of less than 2 dB per connection between a SMF-28 single mode fibre and a silicon photonic chip at these temperatures. This technique has shown robustness to thermal cycling and is ultra-high vacuum compatible without the need for any active alignment

Analytical modeling of silicon microring and microdisk modulators with electrical and optical dynamics

We propose an analytical time-domain model for microring and microdisk modulators, which considers both their electrical and optical properties. Theory of the dynamics of microring/microdisk is discussed, and general solutions to the transfer matrix representation are presented. Both static and dynamic predictions from the model are compared to measurement results to demonstrate the accuracy of our model. Static predictions and measurements are presented for power and phase responses, whereas dynamic predictions and measurements are presented for small-signal and large-signal operations. The model verifies that the chirping and modulation bandwidth of the modulators depend on the detuning state. Finally, the accuracy and scalability of several techniques employed in the model are discussed