Real-time deep learning design tool for far-field radiation profile
Jinran Qie, Erfan Khoram, Dianjing Liu, Ming Zhou, Li Gao
Abstract
The connection between Maxwell’s equations and artificial neural networks has revolutionized the capability and efficiency of nanophotonic design. Such a machine learning tool can help designers avoid iterative, time-consuming electromagnetic simulations and even allows long-desired inverse design. However, when we move from conventional design methods to machine-learning-based tools, there is a steep learning curve that is not as user-friendly as commercial simulation software. Here, we introduce a real-time, web-based design tool that uses a trained deep neural network (DNN) for accurate far-field radiation prediction, which shows great potential and convenience for antenna and metasurface designs. We believe our approach provides a user-friendly, readily accessible deep learning design tool, with significantly reduced difficulty and greatly enhanced efficiency. The web-based tool paves the way to present complicated machine learning results in an intuitive way. It also can be extended to other nanophotonic designs based on DNNs and replace conventional full-wave simulations with a much simpler interface.