Large-scale photonic inverse design: computational challenges and breakthroughs
Abstract: Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design of large-scale photonic structures. It delves into the adequacy of various electromagnetic solvers for large-scale designs, from conventional to neural network-based solvers, and discusses their suitability and limitations. Furthermore, this review evaluates the research on optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and sheds light on cutting-edge studies that combine neural networks with inverse design for large-scale applications. Through this comprehensive examination, this review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Large-scale photonic inverse design: computational challenges and breakthroughs ; volume:13 ; number:20 ; year:2024 ; pages:3765-3792 ; extent:28
Nanophotonics ; 13, Heft 20 (2024), 3765-3792 (gesamt 28)
- Urheber
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Kang, Chanik
Park, Chaejin
Lee, Myunghoo
Kang, Joonho
Jang, Min Seok
Chung, Haejun
- DOI
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10.1515/nanoph-2024-0127
- URN
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urn:nbn:de:101:1-2409011742007.890194013879
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:29 MESZ
Datenpartner
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Beteiligte
- Kang, Chanik
- Park, Chaejin
- Lee, Myunghoo
- Kang, Joonho
- Jang, Min Seok
- Chung, Haejun
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