  Optical spectroscopy is indispensable for research and development in
nanoscience and nanotechnology, microelectronics, energy, and advanced
manufacturing. Advanced optical spectroscopy tools often require both
specifically designed high-end instrumentation and intricate data analysis
techniques. Beyond the common analytical tools, deep learning methods are well
suited for interpreting high-dimensional and complicated spectroscopy data.
They offer great opportunities to extract subtle and deep information about
optical properties of materials with simpler optical setups, which would
otherwise require sophisticated instrumentation. In this work, we propose a
computational ellipsometry approach based on a conventional tabletop optical
microscope and a deep learning model called EllipsoNet. Without any prior
knowledge about the multilayer substrates, EllipsoNet can predict the complex
refractive indices of thin films on top of these nontrivial substrates from
experimentally measured optical reflectance spectra with high accuracies. This
task was not feasible previously with traditional reflectometry or ellipsometry
methods. Fundamental physical principles, such as the Kramers-Kronig relations,
are spontaneously learned by the model without any further training. This
approach enables in-operando optical characterization of functional materials
within complex photonic structures or optoelectronic devices.
