Large area optimization of meta-lens via data-free machine learning
Large area optimization of meta-lens via data-free machine learning
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DOI：
10.1038/s44172-023-00107-x
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发表时间：
2022-12
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通讯作者：
M. Zhelyeznyakov;Johannes E. Fröch;A. Wirth-Singh;Jae-Eok Noh;J. Rho;Steve Brunton;A. Majumdar
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文献类型：
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作者：
M. Zhelyeznyakov;Johannes E. Fröch;A. Wirth-Singh;Jae-Eok Noh;J. Rho;Steve Brunton;A. Majumdar
亚波长衍射光学，通常被称为亚光学，由于其多尺度的性质，提出了一个复杂的数值模拟挑战。组成亚波长散射体或亚原子的行为需要通过全波电磁模拟来建模，而整个亚光学系统可以使用射线/傅立叶光学来建模。大多数大规模元光学的模拟技术依赖于局域相位近似(LPA)，其中忽略了不同元原子之间的耦合。在这里，我们介绍了一个物理信息的神经网络，结合重叠边界方法，它可以有效地模拟元光学，同时仍然包括所有元原子之间的耦合。我们通过设计1 mm口径的柱面隐形透镜证明了我们的技术的有效性，它比在LPA下设计的透镜具有更高的效率。我们通过实验验证了反向设计的准分子透镜的最大强度提升(最高可达53%)。我们报道的方法可以在合理的时间内(在图形处理单元上大约15分钟)设计大口径( ~ 10~4− 10~5λ)亚光学器件，而不依赖于LPA。
Sub-wavelength diffractive optics, commonly known as meta-optics, present a complex numerical simulation challenge, due to their multi-scale nature. The behavior of constituent sub-wavelength scatterers, or meta-atoms, needs to be modeled by full-wave electromagnetic simulations, whereas the whole meta-optical system can be modeled using ray/ Fourier optics. Most simulation techniques for large-scale meta-optics rely on the local phase approximation (LPA), where the coupling between dissimilar meta-atoms is neglected. Here we introduce a physics-informed neural network, coupled with the overlapping boundary method, which can efficiently model the meta-optics while still incorporating all of the coupling between meta-atoms. We demonstrate the efficacy of our technique by designing 1mm aperture cylindrical meta-lenses exhibiting higher efficiency than the ones designed under LPA. We experimentally validated the maximum intensity improvement (up to 53%) of the inverse-designed meta-lens. Our reported method can design large aperture ( ~ 104− 105λ) meta-optics in a reasonable time (approximately 15 minutes on a graphics processing unit) without relying on the LPA.