TMM-Fast, a transfer matrix computation package for multilayer thin-film optimization: tutorial
Luce A, Mahdavi A, Marquardt F, Wankerl H (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 39
Pages Range: 1007-1013
Journal Issue: 6
DOI: 10.1364/JOSAA.450928
Abstract
Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging. An advanced thin-film structure can consist of multiple materials with different thicknesses and numerous layers. Design and optimization of complex thin-film structures with multiple variables is a computationally heavy problem that is still under active research. To enable fast and easy experimentation with new optimization techniques,we propose the Python packageTransferMatrixMethod - Fast (TMM-Fast), which enables parallelized computation of reflection and transmission of light at different angles of incidence and wavelengths through the multilayer thin film.By decreasing computational time, generating datasets for machine learning becomes feasible, and evolutionary optimization can be used effectively. Additionally, the subpackageTMM-Torch allows us to directly compute analytical gradients for local optimization by usingPyTorch Autograd functionality. Finally, an OpenAI Gym environment is presented, which allows the user to train new reinforcement learning agents on the problem of finding multilayer thin-film configurations.
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Germany (DE)How to cite
APA:
Luce, A., Mahdavi, A., Marquardt, F., & Wankerl, H. (2022). TMM-Fast, a transfer matrix computation package for multilayer thin-film optimization: tutorial. Journal of the Optical Society of America A-Optics Image Science and Vision, 39(6), 1007-1013. https://dx.doi.org/10.1364/JOSAA.450928
MLA:
Luce, Alexander, et al. "TMM-Fast, a transfer matrix computation package for multilayer thin-film optimization: tutorial." Journal of the Optical Society of America A-Optics Image Science and Vision 39.6 (2022): 1007-1013.
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