Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network

Abstract

Narrowband perfect absorbers are interesting for spectrum sensing, molecular detection, and infrared imaging. However, their design remains constrained by intuitive, iterative methods that lack flexibility, while also facing challenges in multi-objective optimization. Here, we introduce a deep learning-enabled inverse-design framework that overcomes these limitations through a conditional Wasserstein Generative Adversarial Network (WGAN). The main contribution of this work is a dual-channel image encoding scheme that jointly represents the geometry and thickness of a Si$_3$N$_4$ meta-layer, facilitating the network to learn the distribution of viable structures for a target optical response. This approach naturally solves the inherent ``one-to-many'' design issue, giving a diverse portfolio of functional candidates from a single input spectrum. The designed absorbers achieve exceptional spectral fidelity, with resonance peak errors below 5 nm, a mean squared error (MSE) on the order of $10^{-3}$, and the capacity to produce over 10 distinct, high-performance designs per target. Furthermore, we demonstrate the model's robustness under oblique illumination, showing that it can be efficiently fine-tuned to maintain spectral accuracy across incidence angles from $10^\circ$ to $40^\circ$ by transfer learning, thus extending its practical utility to non-normal operating conditions. Full-wave simulations confirm that the generated geometries support a hybrid plasmonic-dielectric resonance, leading to near-perfect absorption and strong near-field enhancement. Our study provides a robust, physics-aware design paradigm that moves beyond conventional parametric optimization. The introduced framework establishes a versatile platform for the on-demand inverse design of advanced photonic devices for sensing, spectroscopy, and optical signal processing.