# Automated Optical Multi-layer Design via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2006.11940 · HTML: https://mlsanity.com/paper/2006.11940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [A Reinforcement learning method for Optical Thin-Film Design](https://mlsanity.com/paper/2102.09398/) (2021)

## Abstract (arXiv)

Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is large. In this work, we frame the multi-layer optical design task as a sequence generation problem. A deep sequence generation network is proposed for efficiently generating optical layer sequences. We train the deep sequence generation network with proximal policy optimization to generate multi-layer structures with desired properties. The proposed method is applied to two energy applications. Our algorithm successfully discovered high-performance designs, outperforming structures designed by human experts in task 1, and a state-of-the-art memetic algorithm in task 2.