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

TL;DR

TMM-Fast is a Python package that accelerates the design and optimization of multilayer thin-film structures by enabling fast computation, gradient calculation, and reinforcement learning integration.

Contribution

The paper introduces TMM-Fast, a novel Python package that significantly speeds up multilayer thin-film optimization through parallel computation, analytical gradients, and reinforcement learning support.

Findings

Reduced computational time for multilayer analysis

Enabled dataset generation for machine learning applications

Facilitated reinforcement learning for thin-film design

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 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.