Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility

Abstract

. In most cases, the inverse design demonstrates high accuracy with deviations of less than 1.5 nm for critical geometrical parameters. For experimental verification, gold nanohole arrays are fabricated using deep UV lithography. Validation against experimental data demonstrates the models' robustness and precision. These findings show that the trained DNN models offer accurate predictions about the optical behavior of NHAs.

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