Zeng Yunhui, Cao Hongkun, Jin Xin
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Peng Cheng Laboratory, Shenzhen 518055, China.
Nanophotonics. 2025 Jul 15;14(17):2923-2938. doi: 10.1515/nanoph-2025-0210. eCollection 2025 Aug.
Metasurfaces, capable of manipulating light at subwavelength scales, hold great potential for advancing optoelectronic applications. Generative models, particularly Generative Adversarial Networks (GANs), offer a promising approach for metasurface inverse design by efficiently navigating complex design spaces and capturing underlying data patterns. However, existing generative models struggle to achieve high electromagnetic fidelity and structural diversity. These challenges arise from the lack of explicit electromagnetic constraints during training, which hinders accurate structure-to-electromagnetic mapping, and the absence of mechanisms to handle one-to-many mappings dilemma, resulting in insufficient structural diversity. To address these issues, we propose the Anchor-controlled Generative Adversarial Network (AcGAN), a novel framework that improves both electromagnetic fidelity and structural diversity. To achieve high electromagnetic fidelity, AcGAN proposes the Spectral Overlap Coefficient (SOC) for precise spectral fidelity assessment and develops AnchorNet, which provides real-time physics-guided feedback on electromagnetic performance to refine the structure-to-electromagnetic mapping. To enhance structural diversity, AcGAN incorporates a cluster-guided controller that refines input processing and ensures multilevel spectral integration, guiding the generation process to explore multiple configurations. Empirical analysis shows that AcGAN reduces the Mean Squared Error (MSE) by 73 % compared to current state-of-the-art and significantly expands the design space to generate diverse metasurface architectures that meet precise spectral demands.
超表面能够在亚波长尺度上操纵光，在推进光电子应用方面具有巨大潜力。生成模型，特别是生成对抗网络（GAN），通过有效地探索复杂的设计空间并捕捉潜在的数据模式，为超表面逆向设计提供了一种很有前景的方法。然而，现有的生成模型难以实现高电磁保真度和结构多样性。这些挑战源于训练过程中缺乏明确的电磁约束，这阻碍了精确的结构到电磁的映射，以及缺乏处理一对多映射困境的机制，导致结构多样性不足。为了解决这些问题，我们提出了锚定控制生成对抗网络（AcGAN），这是一个新颖的框架，可提高电磁保真度和结构多样性。为了实现高电磁保真度，AcGAN提出了光谱重叠系数（SOC）用于精确的光谱保真度评估，并开发了AnchorNet，它能提供关于电磁性能的实时物理引导反馈，以优化结构到电磁的映射。为了增强结构多样性，AcGAN引入了一个聚类引导控制器，该控制器可优化输入处理并确保多级光谱整合，引导生成过程探索多种配置。实证分析表明，与当前最先进的方法相比，AcGAN将均方误差（MSE）降低了73%，并显著扩展了设计空间，以生成满足精确光谱需求的多样超表面架构。
