论文标题
一种基于生成机器学习的方法，用于多层元面的逆设计
A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces
论文作者
论文摘要
表现出一组特定所需散射特性的元表面的合成是一个耗时且需要资源的过程，该过程通常依赖于许多全波模拟的循环。它要求经验丰富的设计师选择金属层的数量，散射器形状和尺寸以及分离基板的类型和厚度。在这里，我们提出了一种基于生成的机器学习（ML）的方法来求解此一对一的映射并自动化双层和三层元素的逆设计。使用这种方法，可以通过合成由潜在的崭新散射器设计组成的薄结构来解决多目标优化问题，而如果层之间的层间耦合不可忽略，并且通过传统方法合成的层耦合变得很麻烦。提供了各种示例，以提供$ x $的特定幅度和相位响应，以及在频率范围内的$ y $极性散射系数，以及针对不同元表面应用的基于掩模的响应，以验证所提出方法的实用性。
The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the thickness of the separating substrates. Here, we propose a generative machine learning (ML)-based approach to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces. Using this approach, it is possible to solve multiobjective optimization problems by synthesizing thin structures composed of potentially brand-new scatterer designs, in cases where the inter-layer coupling between the layers is non-negligible and synthesis by traditional methods becomes cumbersome. Various examples to provide specific magnitude and phase responses of $x$- and $y$-polarized scattering coefficients across a frequency range as well as mask-based responses for different metasurface applications are presented to verify the practicality of the proposed method.
