论文标题
使用生成机器学习的多波段反射偏振元面的逆设计
Inverse Design of Multi-band Reflective Polarizing Metasurfaces Using Generative Machine Learning
论文作者
论文摘要
具有宽和多波段功能的电磁线性到圆形极化转换器可以简化需要圆形极化的天线系统。多带解决方案在卫星通信系统中具有吸引力，通常需要将极化感逆转{在相邻频段之间}。但是，使用常规\ textit {ad hoc}方法的这些结构的设计在很大程度上取决于经验方法。在这里，我们采用了与生成对抗网络集成的数据驱动方法，以彻底探索偏光层的元素元素的设计空间。使用提出的方法合成双波段和三波形反射极化器，其与入射角度稳定的性能稳定，包括$ 30^\ circ $，{对应于典型的反射器天线系统需求}。设计偏振器的可行性和性能是通过制造原型的测量来验证的。
Electromagnetic linear-to-circular polarization converters with wide- and multi-band capabilities can simplify antenna systems where circular polarization is required. Multi-band solutions are attractive in satellite communication systems, which commonly have the additional requirement that the sense of polarization is reversed {between adjacent bands}. However, the design of these structures using conventional \textit{ad hoc} methods relies heavily on empirical methods. Here, we employ a data-driven approach integrated with a generative adversarial network to explore the design space of the polarizer meta-atom thoroughly. Dual-band and triple-band reflective polarizers with stable performance over incident angles up to and including $30^\circ$, {corresponding to typical reflector antenna system requirements}, are synthesized using the proposed method. The feasibility and performance of the designed polarizer is validated through measurements of a fabricated prototype.
