The transformational dive of nanophotonics inverse design from deep learning to artificial general intelligence
Qizhou Wang,
Yushu Zhang,
Arturo Burguete-Lopez,
Sergei Rodionov,
Andrea Fratalocchi
Affiliations
Qizhou Wang
Primalight, Faculty of Electrical Engineering; Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Yushu Zhang
Primalight, Faculty of Electrical Engineering; Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Arturo Burguete-Lopez
Primalight, Faculty of Electrical Engineering; Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Sergei Rodionov
Primalight, Faculty of Electrical Engineering; Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Andrea Fratalocchi
Primalight, Faculty of Electrical Engineering; Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
The swift development of artificial intelligence (AI) is significantly transforming the paradigm of nanophotonics. Leveraging universal approximation abilities, AI models sidestep time-consuming electromagnetic simulations, opening the inverse design of photonics systems with millions of design features while offering ample stability and practical scalability compared to traditional optimization methods. This perspective discusses inverse design paradigms enabled by recent advances in AI models, discussing their roles, challenges, and opportunities envisioned by the approaching era of artificial general intelligence.
