Deep-learning-enabled optical ellipsometry for complex thin films and 2D materials

Analysis of optical spectroscopy data often requires intensive model fitting. Reflectometry and ellipsometry are commonly used methods to measure the optical dielectric functions or the complex refractive indices of optical thin films such as 2D materials. However, the substrate structure needs to be simple (a single, thick, and transparent substrate is ideal) and perfectly defined. In addition, the available fitting models are extremely computational expensive, and very specific to the optical structures of the samples. In this study, we develop a deep learning method based on a encoder-decoder convolutional neural network that is capable of extracting refractive indices of thin-film materials (including 2D materials) on arbitrary complex multilayer substrates. Kramers-Kronig relations are incorporated into the model to reduce the dimensions of the training parameters. The model is trained using numerically generated data. Without any prior knowledge of stacked material structures, our model can predict the complex refractive indices of 2D materials from experimentally obtained optical reflectance data with high accuracies. This approach enables the in-situ optical characterization of functional materials and components in actual complex optoelectronic devices, a task previously not feasible with traditional reflectometry or ellipsometry methods.