Simulation and modeling of fabricated metasurface optical device measured via polarimetric scatterometer

Diaz, Carlos D.; Burckel, David Bruce; Adomanis, Bryan M.; Urbas, Augustine; Marciniak, Michael A.; Ethridge, James

doi:10.1117/12.2568200

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Publication Year Method(s) Application of AI Sun, Cai, and Wang [450] 2017 PSO optimization framework Huang, Pu, Zhao, et al [451] 2018 GA optimization framework Karaaslan, Bagmancı, Ünal, et al [452] 2018 GA optimization framework Bor, Babayigit, Kurt, et al [453] 2018 GA optimization framework Mayer and Lobet [454] 2018 GA optimization framework Dubajić, Daničić, Vuković, et al [455] 2018 GA optimization framework Franco Rêgo, Gomes De Souza, 2018 GA optimization framework and Rodriguez-Esquerr [456] Thompson and Pisano [188] 2018 micro Genetic Algorithm Inverse design Ren, Liu, Dong, et al [457] 2018 PSO optimization framework Parvez, Rao, and Zanjani [458] 2019 GA Inverse design Blechman, Almeida, Sain, et al [459] 2019 GA optimization framework Zanjani, Chaharmahali, Biabanifard, et al [460] 2019 GA optimization framework Mayer and Lobet [461] 2019 GA optimization framework Ma and Zhang [462] 2019 GA optimization framework Yeh and Harne [463] 2019 GA optimization framework Li, Stan, Czaplewski, et al [187] 2019 micro Genetic Algorithm optimization framework Li, Bao, Jiang, et al [464] 2019 PSO Inverse design Chaharmahali, Soltani, Hoseini, et al [465] 2020 GA Inverse design Shuai, Zhang, Xu, et al [466] 2020 GA Inverse design Jiang and Yi [467] 2020 GA Inverse design Lari, Vafapour, and Ghahraloud [468] 2020 GA optimization framework Cai, Sun, Wang, et al [469] 2020 GA optimization framework Liu, Maier, and Li [470] 2020 GA optimization framework Melo, Ribeiro, Benevides, et al [471] 2020 GA optimization framework Li, Liu, Long, et al [472] 2020 GA optimization framework DIaz, Burckel, Adomanis, et al [473] 2020 GA optimization framework Mayer, Griesse-Nascimento, Bi, et al [474] 2020 GA optimization framework Briones, Carrillo, and Ruiz-Cruz [475] 2020 PSO optimization framework Vafapour, Ghahraloud, Keshavarz, et al [476] 2021 GA optimization framework Jin, Yang, Xu, et al…”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

View full text Add to dashboard Cite

Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

show abstract