Inverse design of plasmonic nanoantenna using generative adversarial network

Qiwen Bao; Dasen Zhang; Xianjin Liu; Licheng Wang; Junjun Xiao

doi:10.1088/1361-6528/acddee

Inverse design of plasmonic nanoantenna using generative adversarial network

Nanotechnology. 2023 Jun 27;34(36). doi: 10.1088/1361-6528/acddee.

Authors

Qiwen Bao¹, Dasen Zhang¹, Xianjin Liu¹, Licheng Wang¹, Junjun Xiao¹

Affiliation

¹ Shenzhen 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.

PMID: 37311448
DOI: 10.1088/1361-6528/acddee

Abstract

The local surface plasmon resonance (LSPR) effect has been widely used in various nanophotonic applications. However, because the LSPR effect is highly sensitive to the structure and geometry, it is desirable to efficiently search viable geometries for predefined local field enhancement spectrum. Herein we present a generative adversarial network-based LSPR nanoantenna design scheme. By encoding the antenna structure information into an red-green-blue (RGB) color image, the corresponding nanoantenna structure can be inverse-designed to achieve the required enhancement spectrum of the local field. The proposed scheme can accurately offer the multiple geometry layout for the customized specific spectrum in seconds, which could be beneficial for fast design and fabrication of plasmonic nanoantenna.

Keywords: deep learning; generative adversarial network; inverse design; plasmonic nanoantenna.