Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.

中文翻译：

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通过优化和机器学习设计的分束双各向异性超表面


电磁超表面由于其低调和有利的应用而最近引起了人们的极大兴趣。实际上，许多超表面设计都是从一组辐射远场的约束开始的，例如主波束方向和旁瓣水平，并以表面的不均匀物理结构结束。这个问题非常具有挑战性，因为当仅对散射场施加约束时，所需的切向场变换并不完全已知。因此，无法通过分析求解所需的表面特性。此外，将所需的表面属性转换为物理晶胞可能既耗时又困难，因为它通常是大解空间中的一对多映射。在这里，我们将逆向设计过程分为两个步骤：宏观设计步骤和微观设计步骤。在前者中，我们使用迭代优化过程来查找辐射符合指定约束的远场图案的表面属性。这个迭代过程利用非辐射电流来确保无源和无损设计。在微观步骤中，这些优化的表面特性是通过使用机器学习代理模型的物理单元实现的。通过分束原型的测量结果证明了这种端到端综合过程的有效性。