# Multifunctional Metasurface Design with a Generative Adversarial Network

arXiv: https://arxiv.org/abs/1908.04851 · HTML: https://mlsanity.com/paper/1908.04851/

## Key facts

- Cited by 14 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning](https://mlsanity.com/paper/2202.10565/) (2022)
- [Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design](https://mlsanity.com/paper/2304.13038/) (2023)
- [Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces](https://mlsanity.com/paper/2102.01761/) (2021)
- [Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning](https://mlsanity.com/paper/2401.00003/) (2023)
- [Data driven approaches in nanophotonics: A review of AI-enabled metadevices](https://mlsanity.com/paper/2510.00283/) (2025)
- [Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design](https://mlsanity.com/paper/2408.16231/) (2024)
- [Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media](https://mlsanity.com/paper/2511.05357/) (2025)
- [Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning](https://mlsanity.com/paper/2204.00433/) (2022)
- [Generating extreme quantum scattering in graphene with machine learning](https://mlsanity.com/paper/2212.06929/) (2022)
- [Performance Comparison of Design Optimization and Deep Learning-based Inverse Design](https://mlsanity.com/paper/2308.13000/) (2023)
- [A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design](https://mlsanity.com/paper/2401.02961/) (2023)
- [Deep Electromagnetic Structure Design Under Limited Evaluation Budgets](https://mlsanity.com/paper/2506.19384/) (2025)
- [Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design](https://mlsanity.com/paper/2606.09266/) (2026)

## Abstract (arXiv)

Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta-atom structures based on specific requirements (commonly formulated in terms of electromagnetic responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, we present a Generative Adversarial Networks (GAN) that can tackle this problem and generate meta-atom/metasurface designs to meet multifunctional design goals. Unlike conventional trial-and-error or iterative optimization design methods, this new methodology produces on-demand free-form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar electromagnetic responses but different physical features could provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. We demonstrate the network's ability to produce a variety of multifunctional metasurface designs by presenting a bifocal metalens, a polarization-multiplexed beam deflector, a polarization-multiplexed metalens and a polarization-independent metalens.