Mapping the Design Space of Photonic Topological States via Deep Learning

Abstract

Topological states in photonics offer novel prospects for guiding and manipulating photons and facilitate the development of modern optical components for a variety of applications. Over the past few years, photonic topology physics has evolved and unveiled various unconventional optical properties in these topological materials, such as silicon photonic crystals. However, the design of such topological states still poses a significant challenge. Conventional optimization schemes often fail to capture their complex high dimensional design space. In this manuscript, we develop a deep learning framework to map the design space of topological states in the photonic crystals. This framework overcomes the limitations of existing deep learning implementations. Specifically, it reconciles the dimension mismatch between the input (topological properties) and output (design parameters) vector spaces and the non-uniqueness that arises from one-to-many function mappings. We use a fully connected deep neural network (DNN) architecture for the forward model and a cyclic convolutional neural network (cCNN)for the inverse model. The inverse architecture contains the pre-trained forward model in tandem, thereby reducing the prediction error significantly.

3 figures, AI for better photonics, topological photonics, inverse design

References in corpus (2)

• Topological Photonics
• Global optimization of dielectric metasurfaces using a physics-driven neural network

Cited by in corpus (4)

• Deep neural networks for the evaluation and design of photonic devices
• Intelligent metaphotonics empowered by machine learning
• Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity
• LLM4Laser: Large Language Models Automate the Design of Lasers