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Inverse design of plasmonic nanoantenna using generative adversarial network
Qiwen Bao1, Dasen Zhang1, Xianjin Liu1
1Shenzhen Engineering Laboratory of Aerospace Detection and Imaging, College of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, People's Republic of China.
Nanotechnology
|June 13, 2023
Summary
We developed a generative adversarial network to design plasmonic nanoantennas. This AI approach rapidly identifies optimal nanoantenna geometries for specific local surface plasmon resonance (LSPR) enhancement spectra.
Area of Science:
- Nanophotonics
- Plasmonics
- Artificial Intelligence
Background:
- Local surface plasmon resonance (LSPR) is crucial for nanophotonic applications.
- LSPR sensitivity to structure necessitates efficient geometry searching for desired field enhancement spectra.
Purpose of the Study:
- To present a generative adversarial network (GAN)-based scheme for inverse-designing LSPR nanoantennas.
- To enable rapid identification of nanoantenna geometries for specific local field enhancement spectra.
Main Methods:
- Encoding nanoantenna structure information into red-green-blue (RGB) color images.
- Utilizing a GAN for inverse design to achieve target enhancement spectra.
Main Results:
- The proposed scheme accurately provides multiple geometry layouts for customized spectra.
- The design process is completed in seconds, demonstrating high efficiency.
Conclusions:
- The GAN-based scheme offers a fast and effective method for designing plasmonic nanoantennas.
- This approach accelerates the design and fabrication of nanoantennas for tailored plasmonic responses.
