Convolutional neural networks used for random structure SPP gratings spectral response prediction

Area of Science:

• Nanophotonics
• Computational electromagnetics
• Machine learning

Background:

• Traditional nanophotonic device design relies on time-consuming iterative simulations.
• Predicting the spectral response of nanophotonic structures, like surface plasmon polariton (SPP) gratings, is computationally intensive.
• Existing deep learning approaches often focus on modifying parameters of regular, predefined structures.

Purpose of the Study:

• To develop a fast and accurate prediction model for SPP grating output spectra using deep learning.
• To overcome the limitations of predefined shapes in previous data-driven nanophotonic design.
• To introduce a novel method for random structure design in nanophotonics.

Main Methods:

• A convolutional neural network (CNN) was designed and trained for spectral prediction.
• The CNN model was applied to predict output spectra for randomly generated SPP grating structures.
• Model predictions were validated against results from traditional finite-difference time-domain (FDTD) simulations.

Main Results:

• The CNN model achieved effective prediction of SPP grating output spectra for random structures.
• Simulation results showed excellent agreement with the CNN model's predictions.
• The CNN approach demonstrated significant speed advantages over the conventional FDTD method.

Conclusions:

• Deep learning, specifically CNNs, offers a powerful and efficient alternative to traditional simulation methods in nanophotonics.
• The proposed random structure design approach using CNNs provides a new paradigm for nanophotonic device innovation.
• This work accelerates the design cycle for nanophotonic devices by enabling rapid spectral prediction.