TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization
This work provides a tool for researchers in optics and materials science to accelerate thin-film design, though it is incremental as it builds on existing transfer matrix methods with added computational efficiency and integration capabilities.
The authors tackled the computationally heavy problem of designing and optimizing multilayer thin-film structures for optical responses by developing TMM-Fast, a Python package that enables parallelized computation of reflection and transmission, reducing computational time to facilitate machine learning dataset generation and evolutionary optimization.
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 package 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 sub-package TMM-Torch allows to directly compute analytical gradients for local optimization by using PyTorch Autograd functionality. Finally, an OpenAi Gym environment is presented which allows the user to train reinforcement learning agents on the problem of finding multilayer thin-film configurations.