On-demand design of spectrally selective multi-band absorbers using deep learning

Abstract

We report an approach assisted by deep learning to design spectrally-sensitive multi-band absorbers that work in the visible range. We propose a five-layered metal-insulator-metal grating structure composed of aluminum and silicon dioxide, and design its structural parameters by using an artificial neural network (ANN). For a spectrally-sensitive design, spectral information of resonant wavelengths is additionally provided as input as well as the reflection spectrum. The ANN facilitates highly robust design of grating structure that has an average mean squared error of 0.023. The optical properties of the designed structures are validated using electromagnetic simulations and experiments. Analysis of design results for gradually-changing target wavelengths of input show that the trained ANN can learn physical knowledge from data. We also propose a method to reduce the size of the ANN by exploiting observations of the trained ANN for practical applications.