Inverse Design of a Dual-Band Reflective Polarizing Surface Using Generative Machine Learning

Abstract

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 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 • , 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.

Generative Model for Dual-Band Filters Based on Modified Complementary Split-Ring Resonators

“…By representing the circuit prototypes with pixelated patterns and establishing the relationship between the two-dimensional patterns and the electromagnetic behaviors of these modified CSRRs, similar prototypes can be generated to meet the required EM specifications. In the generative model employed for inverse designs, the utilization of generative adversarial networks (GANs) has aroused significant attention in recent years [26][27][28][29][30][31]. The incorporation of convolutional neural networks (CNNs) within GAN-based architectures has demonstrated effectiveness in achieving successful inverse designs in various image-processing tasks.…”

Inverse Design of Electromagnetic Metasurfaces Utilizing Infinite and Separate Latent Space Yielded by a Machine Learning-Based Generative Model

“…Furthermore, the process relies heavily on a limited number of designers, with expertise in electromagnetism and intuitive insights derived from extensive experience, who can efficiently narrow down the high-dimensional solution space. In this context, the inverse design technique, which attempts to directly predict equivalent metasurfaces for specific scattering properties, has been attracting considerable attention from many interested researchers, the majority of whom have been encouraged by machine learning (ML)-based frameworks [13][14][15][16][17][18][19][20][21][22][23].…”

Representation Learning-Driven Fully Automated Framework for the Inverse Design of Frequency-Selective Surfaces

“…According to the automation level, we roughly divide them into semi-automated [9]- [19] and fully-automated methods [20]- [22]. Considering the strategy, there are two categories: some works [9]- [16], [21], [22] developed a forward mapping surrogate model and performed iterative optimization to find the optimal design; the others [17]- [20] directly established an inverse mapping surrogate model or utilized generative networks to produce the satisfying design.…”