# A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

arXiv: https://arxiv.org/abs/2008.02074 · HTML: https://mlsanity.com/paper/2008.02074/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure](https://mlsanity.com/paper/2503.01046/) (2025)
- [A Machine Learning Generative Method for Automating Antenna Design and Optimization](https://mlsanity.com/paper/2203.11698/) (2022)
- [Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning](https://mlsanity.com/paper/2204.00433/) (2022)
- [A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design](https://mlsanity.com/paper/2401.02961/) (2023)
- [Image Classifier Based Generative Method for Planar Antenna Design](https://mlsanity.com/paper/2401.06149/) (2023)
- [Meta-training of diffractive meta-neural networks for super-resolution direction of arrival estimation](https://mlsanity.com/paper/2509.05926/) (2025)
- [Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers](https://mlsanity.com/paper/2605.19611/) (2026)
- [Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs](https://mlsanity.com/paper/2606.05849/) (2026)
- [HALO: A Physics-Aware LLM Agent Framework for Nanophotonic Design](https://mlsanity.com/paper/2608.28877/) (2026)
- [Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments](https://mlsanity.com/paper/2608.29907/) (2026)

## Abstract (arXiv)

The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the thickness of the separating substrates. Here, we propose a generative machine learning (ML)-based approach to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces. Using this approach, it is possible to solve multiobjective optimization problems by synthesizing thin structures composed of potentially brand-new scatterer designs, in cases where the inter-layer coupling between the layers is non-negligible and synthesis by traditional methods becomes cumbersome. Various examples to provide specific magnitude and phase responses of x- and y-polarized scattering coefficients across a frequency range as well as mask-based responses for different metasurface applications are presented to verify the practicality of the proposed method.