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Achieving the desired optical response from a multilayer thin-film structureover a broad range of wavelengths and angles of incidence can be challenging.An advanced thin-film structure can consist of multiple materials withdifferent thicknesses and numerous layers. Design and optimization of complexthin-film structures with multiple variables is a computationally heavy problemthat is still under active research. To enable fast and easy experimentationwith new optimization techniques, we propose the Python package TMM-Fast whichenables parallelized computation of reflection and transmission of light atdifferent angles of incidence and wavelengths through the multilayer thin-film.By decreasing computational time, generating datasets for machine learningbecomes feasible and evolutionary optimization can be used effectively.Additionally, the sub-package TMM-Torch allows to directly compute analyticalgradients for local optimization by using PyTorch Autograd functionality.Finally, an OpenAi Gym environment is presented which allows the user to trainreinforcement learning agents on the problem of finding multilayer thin-filmconfigurations.

TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization