On the Benefit of Attention in Inverse Design of Thin Films Filters

Abstract

Attention layers are a crucial component in many modern deep learning models, particularly those used in natural language processing and computer vision. Attention layers have been shown to improve the accuracy and effectiveness of various tasks, such as machine translation, image captioning, etc. Here, the benefit of attention layers in designing optical filters based on a stack of thin film materials is investigated. The superiority of Attention layers over fully-connected Deep Neural Networks is demonstrated for this task.