Free-form optimization of nanophotonic devices: from classical methods to deep learningopen access
- Authors
- Park, Juho; Kim, Sanmun; Nam, Daniel Wontae; Chung, Haejun; Park, Chan Y; Jang, Min Seok
- Issue Date
- May-2022
- Publisher
- WALTER DE GRUYTER GMBH
- Keywords
- adjoint method; free-form optimization; machine learning; photonic device design; reinforcement learning
- Citation
- NANOPHOTONICS, v.11, no.9, pp.1809 - 1845
- Indexed
- SCIE
SCOPUS
- Journal Title
- NANOPHOTONICS
- Volume
- 11
- Number
- 9
- Start Page
- 1809
- End Page
- 1845
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/187289
- DOI
- 10.1515/nanoph-2021-0713
- ISSN
- 2192-8606
- Abstract
- Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conventional design constraints and utilize the full design potential. In this review, we systematically overview the nascent yet rapidly growing field of free-form nanophotonic device design. We attempt to define the term "free-form" in the context of photonic device design, and survey different strategies for free-form optimization of nanophotonic devices spanning from classical methods, adjoint-based methods, to contemporary machine-learning-based approaches.
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Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
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