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

arXiv: https://arxiv.org/abs/2111.13667 · HTML: https://mlsanity.com/paper/2111.13667/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Reinforcement Learning with Parameterized Actions](https://mlsanity.com/paper/1509.01644/) (2015)
- [Parameterized Reinforcement Learning for Optical System Optimization](https://mlsanity.com/paper/2010.05769/) (2020)

## Built on by (most cited first, 5 of 5)

- [Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks](https://mlsanity.com/paper/2210.04629/) (2022)
- [Generative Inverse Design with Abstention via Diagonal Flow Matching](https://mlsanity.com/paper/2603.15925/) (2026)
- [PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design](https://mlsanity.com/paper/2605.26502/) (2026)
- [Playing Ping Pong with Light: Directional Emission of White Light](https://mlsanity.com/paper/2111.15486/) (2021)
- [Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping](https://mlsanity.com/paper/2507.18644/) (2025)

## Abstract (arXiv)

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.