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-c...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.展开更多
基金National Natural Science Foundation of China(61974069,62022043)Natural Science Foundation of Jiangsu Province(BK20191379)+1 种基金NUPTSF(NY219008)NJUPT 1311 Talent Program.
文摘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.
