Abstract

Data-driven design approaches based on deep learning have been introduced into nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report a convolutional neural network (CNN) used to perform the prediction of surface plasmon polariton (SPP) grating output spectra, which is not limited by predefined shapes. For a random given structure, the network can output spectra with effective prediction, so that the simulation results are in excellent agreement with the network prediction results. Compared with the traditional finite-difference time-domain (FDTD) method, the CNN model proposed in this Letter has absolute advantages in speed. Previous studies often used a regular device structure to modify its parameters for prediction; the random structure design method adopted in this Letter also provides a new, to the best of knowledge, idea for device design.

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