Absorptive angle-selective surfaces

• Main Author: Qin, Xiong
• Other Authors: Shen Zhongxiang
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Frequency selective surface; Metamaterial; Meta-surface; Antenna; Electromagnetic selective surface
• Online Access: https://hdl.handle.net/10356/173716
• Institution: Nanyang Technological University

This study delves into the innovative design and simulation of absorptive angle-selective surfaces (AASS) implemented based on periodically arrangements of planar conductive elements, presenting a paradigm shift in spatial filtering of electromagnetic waves. The crux of our approach lies in the meticulous network analysis of multi-layer periodic structures, comprising N layers of periodic structures and N − 1 air spacers. This analytical model allows for precise determination of each layer’s complex impedance, pivotal for achieving desired transmission, reflection, and absorption characteristics. We propose a dual-layer polarization-insensitive AASS model, where one layer exhibits resistive properties, achieving high absorption near the operating frequencies, while the other, a conductive layer, facilitates significant reflection (reflective AASS) or transmission (transmissive AASS). Our study extends to include the exploration of unit cells, where cross-dipoles are identified as optimal for realizing angular selectivity. Computational simulations have been conducted to validate our theoretical models. Results demonstrate a notable absorptive bandwidth near 10 GHz, with significant backward angular selectivity, rendering the design ideal for diverse applications, including but not limited to, radar cross-section reduction and wireless communications. This comprehensive analysis and simulation not only bolster the understanding of AASS but also open avenues for future research, particularly in enhancing bandwidth and angular domain behavior. The amalgamation of theoretical modeling and empirical simulations in this study provides a robust foundation for the development of advanced spatial filtering technologies.