The accelerated design of the nanoantenna arrays by deep learning

Abstract

Abstract Nanoantenna fusion photonics and nanotechnology can manipulate light through the ultra-thin structure composed of sub-wavelength antennas, and meet the important requirements for miniaturized optical components, completely changing the field of optics. However, the device design process is still time-consuming and consumes computing resources. Besides, the professional knowledge requirements of engineers are also high. Relying on the algorithm’s inference ability and excellent computing ability, artificial intelligence has great potential in the fields of material design, material screening, and device performance prediction. However, the deep learning (DL) requires a mass of data. Therefore, this article proposes a method for the forward and inverse design of nanoantenna based on DL. Compared with the previous work, the network uses a two-dimensional matrix as input, which has a simple structure and is more suitable for the advantages of deep netural network. Simultaneously, the small datasets can be used to achieve higher accuracy. In the forward prediction, 100% of the data error is less than 0.007; in the inverse prediction, the data with error less than 0.05 accounted for 90%, 99.8% and 100% of the length, height, and width’s datasets. It demonstrates that the method can improve the automation of the design process and reduce the consumption of computer resources.