AI for accelerated simulations of plasmonic nanostructures

The spectral responses of large aperiodic plasmonic metasurfaces were observed to hold essential importance for the practical and theoretical studies. Recent developments in machine-learning approaches have proved effective in determining the optical properties of nanostructures. In particular, a neural network has been designed and trained to predict the spectral response and near-field distribution of complex multiscale patterns of plasmonic nanostructures. This technique neatly accounts for spectral coupling in the far- or near-field domain with a computational saving (x100) compared to traditional Maxwell solvers. As such, the neural-network representation provides a promising path forward for fast design and optimization of plasmonic nanostructures and metasurfaces.

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