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

doi:10.1063/5.0226592

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English (US)
Article number	100902
Journal	APL Photonics
Volume	9
Issue number	10
DOIs	https://doi.org/10.1063/5.0226592
State	Published - Oct 1 2024

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Computer Networks and Communications

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10.1063/5.0226592

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The transformational dive of nanophotonics inverse design from deep learning to artificial general intelligence

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