التفاصيل البيبلوغرافية
| العنوان: |
Comparison of Different Neural Network Architectures for Plasmonic Inverse Design |
| المؤلفون: |
Qingxin Wu (8166069), Xiaozhong Li (715315), Wenqi Wang (610469), Qiao Dong (9961598), Yibo Xiao (9255351), Xinyi Cao (3398783), Lianhui Wang (831497), Li Gao (131516) |
| سنة النشر: |
2021 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Neuroscience, Evolutionary Biology, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, tandem neural network, shown unprecedented capability, iterative neural network, efficient forward modeling, deep neural network, inverse design process, plasmonic inverse design, accurate inverse design, appropriate network architecture, inverse design, sufficiently accurate, plasmonic nanoantenna, complex architecture, well known, training method, successfully trained, results provide, nonuniqueness problem, field spectrum |
| الوصف: |
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 network can both be used in the inverse design process, where the latter is well known for tackling the nonuniqueness problem at the expense of more complex architecture. However, we are curious to compare these two networks’ performance when they are both applicable. Here, we successfully trained both networks to inverse design the far-field spectrum of plasmonic nanoantenna, and the results provide some guidelines for choosing an appropriate, sufficiently accurate, and efficient neural network architecture. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/journal_contribution/Comparison_of_Different_Neural_Network_Architectures_for_Plasmonic_Inverse_Design/16542448 |
| DOI: |
10.1021/acsomega.1c02165.s001 |
| الاتاحة: |
https://doi.org/10.1021/acsomega.1c02165.s001 |
| Rights: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.AD3AAD6E |
| قاعدة البيانات: |
BASE |