Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks

Image processing can be used to extract meaningful optical results from images. Here, from images of plasmonic structures, we combined convolutional neural networks with recurrent neural networks to extract the absorption spectra of structures. To provide the data required for the model, we performed 100,000 simulations with similar setups and random structures. In designing this deep network, we created a model that can predict the absorption response of any structure with a similar setup. We used convolutional neural networks to collect spatial information from the images, and then, we used that data and recurrent neural networks to teach the model to predict the relationship between the spatial information and the absorption spectrum. Our results show that this image processing method is accurate and can be used to replace time- and computationally-intensive numerical simulations. The trained model can predict the optical results in less than a second without the need for a strong computing system. This technique can be easily extended to cover different structures and extract any other optical properties.