Abstract

Plasmons have received intensive attention owing to their significant spectrum shift in environmental sensing. Experimentally, the same spectral shifts may be caused by different combinations of structural parameters of a plasmonic nanoparticle. This multi-parameter problem cannot be solved by just a single feature analysis, but requires using the full scattering spectrum containing all features of the parameters. In this Letter, a deep learning method for solving multi-parameter problems is proposed based on the layer refractive index ($n$) and layer thickness ($d$) sensing of different nanorods and nanospheres. The full scattering spectrum can be theoretically simulated, precisely predicted using a well-trained deep learning method, and experimentally obtained using a homemade dark-field microscope. An error analysis of the simulation and experimental results indicates that this method is a potential way to determine $n$ and $d$ and further solve multi-parameter in plasmon sensing.

© 2021 Optica Publishing Group

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