Photon density of states engineering with generative inverse design for scalable 3D photonic metamaterials

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 parameters for target pDOS features. 3D structures featuring high local pDOS were obtained across a broad normalized frequency range and consistently outperformed those in the original dataset. Structural analysis revealed that these high pDOS architectures fall into two predominant structural categories with similar rotational symmetry characteristics. Our work establishes the first inverse design strategy for 3D photonic metamaterials fabricated via holographic lithography.