Advancing statistical learning and artificial intelligence in nanophotonics inverse design

نویسندگان

چکیده

Abstract Nanophotonics inverse design is a rapidly expanding research field whose goal to focus users on defining complex, high-level optical functionalities while leveraging machines search for the required material and geometry configurations in sub-wavelength structures. The journey of begins with traditional optimization tools such as topology heuristics methods, including simulated annealing, swarm optimization, genetic algorithms. Recently, blossoming deep learning various areas data-driven science engineering has begun permeate nanophotonics intensely. This review discusses state-of-the-art optimizations learning, more recent hybrid techniques, analyzing advantages, challenges, perspectives both an engineering.

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ژورنال

عنوان ژورنال: Nanophotonics

سال: 2021

ISSN: ['2192-8606', '2192-8614']

DOI: https://doi.org/10.1515/nanoph-2021-0660