Machine-learning design of graphene nanoribbon waveguide side-coupled absorber

Abstract

Machine learning is emerging as a new approach that provides more options for solving complex problems involving electromagnetic phenomena. This paper evaluates the application of machine learning to the design of graphene-based absorbers, which is a research challenge. Five machine learning algorithms - k-nearest neighbor regression (kNN), artificial neural network (ANN), decision tree (DT), extremely randomized trees (ETs) and random forest (RF) - are applied to realize the transmission spectrum prediction and reverse design of a graphene nanoribbon waveguide side-coupled absorber. The results show that all five algorithms are effective, with RF being the most accurate in the inverse design. Compared with previous work, the application of machine learning in the intelligent design of graphene absorbers is evaluated more comprehensively, providing a reference for the selection of machine learning algorithms for future inverse design problems.