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Recent introduction of data-driven approaches based on deep-learningtechnology has revolutionized the field of nanophotonics by allowing efficientinverse design methods. In this paper, simultaneous inverse design of materialsand structure parameters of core-shell nanoparticle is achieved for the firsttime using deep-learning of a neural network. A neural network to learncorrelation between extinction spectra of electric and magnetic dipoles andcore-shell nanoparticle designs, which include material information and shellthicknesses, is developed and trained. We demonstrate deep-learning-assistedinverse design of core-shell nanoparticle for 1) spectral tuning electricdipole resonances, 2) finding spectrally isolated pure magnetic dipoleresonances, and 3) finding spectrally overlapped electric dipole and magneticdipole resonances. Our finding paves the way of the rapid development ofnanophotonics by allowing a practical utilization of a deep-learning technologyfor nanophotonic inverse design.

Simultaneous inverse design of materials and parameters of core-shell nanoparticle via deep-learning: Demonstration of dipole resonance engineering