Deep neural network for designing near- and far-field properties in plasmonic antennas

The electromagnetic response of plasmonic nanostructures is highly sensitive to their geometric parameters. In multi-dimensional parameter space, conventional full-wave simulation and numerical optimization can consume significant computation time and resources. It is also highly challenging to find the globally optimized result and perform inverse design for a highly nonlinear data structure. In this work, we demonstrate that a simple multi-layer perceptron deep neural network can capture the highly nonlinear, complex relationship between plasmonic geometry and its near-and far-field properties. Our deep learning approach proves accurate inverse design of near-field enhancement and far-field spectrum simultaneously, which can enable the design of dual-functional optical sensors. Such implementation is helpful for exploring subtle, complex multifunctional nanophotonics for sensing and energy conversion applications. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This study demonstrates that deep learning can achieve inverse design of plasmonic nanostructures, enabling the design of dual-functional optical sensors. This method is helpful for exploring multifunctional applications in nanophotonics.