Reinforcement Learning for Photonic Component Design

Abstract

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.

Published version: 9 pages, 12 figures

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• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Deep Learning for Anomaly Detection: A Survey
• PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
• The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

Cited by in corpus (1)

• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization