Comparison of Different Neural Network Architectures for Plasmonic Inverse Design

[Image: see text] The merge between nanophotonics and a deep neural network has shown unprecedented capability of efficient forward modeling and accurate inverse design if an appropriate network architecture and training method are selected. Commonly, an iterative neural network and a tandem neural...

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Detalles Bibliográficos
Autores principales: Wu, Qingxin, Li, Xiaozhong, Wang, Wenqi, Dong, Qiao, Xiao, Yibo, Cao, Xinyi, Wang, Lianhui, Gao, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444196/
https://www.ncbi.nlm.nih.gov/pubmed/34549108
http://dx.doi.org/10.1021/acsomega.1c02165

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444196/
https://www.ncbi.nlm.nih.gov/pubmed/34549108
http://dx.doi.org/10.1021/acsomega.1c02165

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