Photon density of states engineering with generative inverse design for scalable 3D photonic metamaterials
Zesen Zhou, Jeevan Rois, Matias Kagias

TL;DR
This paper introduces an inverse design framework using generative adversarial networks to engineer the photon density of states in 3D photonic metamaterials, enabling scalable fabrication of tailored optical properties.
Contribution
It presents the first inverse design strategy for 3D photonic metamaterials fabricated via holographic lithography, combining a surrogate model and cGAN for targeted pDOS engineering.
Findings
High local pDOS structures achieved across broad frequency range.
High pDOS architectures exhibit similar rotational symmetry features.
Outperformed original dataset in pDOS performance.
Abstract
The photon density of states (pDOS) governs fundamental light matter interactions and is a critical parameter for designing next generation light driven technologies such as photocatalysis and solar energy harvesting. Achieving a target pDOS in 3D nanoarchitected structures remains challenging due to the nonlinear and non unique relationship between geometry and spectral response. Here, we present an end to end inverse design framework for tailoring the pDOS of 3D photonic metamaterials fabricated via the scalable nanofabrication approach of metasurface-based holographic lithography. A data driven forward surrogate model is constructed to predict frequency resolved pDOS spectra from metasurface diffraction parameters and lithographic thresholds. Inverse design is performed using a conditional generative adversarial network (cGAN) that generates candidate metasurface diffraction…
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