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  1. Herein, deep learning (DL) is used to predict the structural parameters of Ag nanohole arrays (NAs) for spectrum‐driving and color‐driving plasmonic applications. A dataset of transmission spectra and structural parameters of NAs is generated using finite‐difference time‐domain (FDTD) calculations and is converted to vivid structural colors using the corresponding transmission spectrum. A bidirectional neural network is used to train the transmission spectrum and structural color together. The accuracy of predicting the structural parameters using a desired spectrum is tested and found to be up to 0.99, with a determination coefficient of reproducing the desired spectrum and color to be 0.97 and 0.96, respectively. These values are higher compared to those when only training for spectrum, but requiring less training time. This strategy is able to inverse design the NAs in less than 1 s to maximize surface‐enhanced Raman scattering (SERS) enhancement by matching transmission resonance and laser excitation wavelength, and accurately regenerate colored images in 7.5 s, allowing for nanoscale printing at a resolution of approximately 100 000 dots in−1. This work has important implications for the efficient design of nanostructures for various plasmonic applications, such as plasmonic sensors, optical filters, metal‐enhanced fluorescence, SERS, and super‐resolution displays.

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  2. Abstract

    Gradient plasmonic nanostructures are produced by a straightforward and powerful fabrication strategy—deposition on curved nanomask (DCNM), a physical vapor deposition on a curved mask substrate covered with a monolayer of close‐packed nanospheres. The feasibility of the DCNM strategy is demonstrated by producing well‐ordered Ag gradient single/double nanotriangle (NT) arrays with continuously adjustable color, extinction, localized surface plasmon resonance wavelength, and surface enhanced Raman scattering (SERS). The plasmonic property and the structure gradient are controlled by the size of the mask and the curvature of the curved substrate, as well as the deposition configuration. A plasmonic library of the single/double NT arrays is easily established in a single fabrication. The DCNM strategy can in principle produce a wide range of gradient nanostructures and further be used for flexible components in optical devices, tunable plasmonic SERS sensors, as well as high‐throughput screening of nanostructures.

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