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arXiv 2602.03062physics.optics
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通过条件Wasserstein生成对抗网络实现可调谐红外超材料吸收体的反向设计

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

AI总结:

本文提出基于条件Wasserstein生成对抗网络的反向设计框架,实现可调谐红外超材料吸收体的高效设计,解决多目标优化难题,提升光谱保真度和设计多样性。

摘要:

窄带完美吸收体在光谱传感、分子检测和红外成像中具有重要价值。然而,其设计仍然受到直观迭代方法的限制,缺乏灵活性,同时面临多目标优化的挑战。本文介绍了一种深度学习赋能的反向设计框架,通过条件Wasserstein生成对抗网络(WGAN)克服这些限制。本工作的主要贡献是一个双通道图像编码方案,共同表示Si₃N₄超材料层的几何形状和厚度,使网络能够学习可行结构的分布以实现目标光学响应。该方法自然解决了固有“一对一多”设计问题,从单个输入光谱中生成多样化的功能候选者。设计的吸收体实现了卓越的光谱保真度,共振峰误差低于5纳米,均方误差(MSE)在10⁻³量级,且能够为每个目标生成超过10种不同、高性能的设计。此外,我们展示了模型在斜入射下的鲁棒性,证明其可以通过迁移学习高效微调,以在入射角从10°到40°范围内保持光谱准确性,从而扩展其在非正常工作条件下的实际应用。全波仿真证实,生成的几何结构支持混合等离子体-介电共振,导致近完美吸收和强近场增强。本研究提供了一种稳健、物理感知的设计范式,超越了传统参数优化。所引入的框架建立了一个多功能平台,用于按需反向设计先进的光子器件,用于传感、光谱学和光学信号处理。

英文摘要:

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.

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