Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network
State Key Laboratory of Integrated Service Networks, Xidian University（西安电子科技大学集成服务网络国家重点实验室）；Air Defense and Antimissile School, Air Force Engineering University（空军工程大学防空反导学院）；Inter disciplinary Center for Quantum Information, College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院量子信息交叉研究中心）
H. Shen, T. Wang, X. Yao, O. Wu, C. Xie, C. Qian, H. Chen, T. Wang
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.
Direct visualization of non-equilibrium heat extraction by a metasurface absorber
Tokyo University of Agriculture and Technology（东京农工大学）；University of Birmingham（伯明翰大学）；RIKEN Center for Advanced Photonics（理化学研究所先进光子研究中心）；RIKEN Cluster for Pioneering Research（理化学研究所先锋研究集群）；Gakushuin University（学习院大学）；Tokushima University（德岛大学）
Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs
Centre for Electromagnetics, CSIR-National Aerospace Laboratories（电磁研究中心，国家航空航天实验室）；Birla Institute of Technology and Science, Pilani, Rajasthan（比拉理工学院和科学学院，比里尼）；Indian Institute of Technology, Bombay, Maharashtra（孟买印度理工学院，马哈拉施特拉）
