Towards Neural Network Classification of Terahertz Measurements for Determining the Number of Coating Layers

Abstract

To determine layer thicknesses with terahertz time-domain spectroscopy, the number of layers must usually be known. However, in some applications the number of layers varies along the surface, so that the number of layers at a specific measuring location can be unknown. Our approach is to use an artificial deep neural network for estimating the number of layers at a preliminary stage for common terahertz algorithms. This work describes the selection and evaluation of a feedforward neural network. This neural network allows a good estimation of the number of layers confirming the usefulness of the proposed approach.