Predicting resonant properties of plasmonic structures by deep learning

TL;DR

This paper presents a deep learning approach combining CNNs and RNNs to accurately predict the absorption properties of plasmonic structures from images, significantly reducing computation time.

Contribution

It introduces a novel deep neural network architecture that effectively predicts plasmonic absorption from images, trained on extensive simulated data.

Findings

High accuracy in absorption prediction compared to numerical simulations

Rapid prediction enabling real-time analysis

Effective use of combined CNN and RNN architecture

Abstract

Deep learning can be used to extract meaningful results from images. In this paper, we used convolutional neural networks combined with recurrent neural networks on images of plasmonic structures and extract absorption data form them. To provide the required data for the model we did 100,000 simulations with similar setups and random structures. By designing a deep network we could find a model that could predict the absorption of any structure with similar setup. We used convolutional neural networks to get the spatial information from the images and we used recurrent neural networks to help the model find the relationship between the spatial information obtained from convolutional neural network model. With this design we could reach a very low loss in predicting the absorption compared to the results obtained from numerical simulation in a very short time.