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Overview of Approaches for Compensating Inherent Metamaterials Losses

Abstract

Metamaterials are synthetic composite structures with extraordinary electromagnetic properties not readily accessible in ordinary materials. These media attracted massive attention due to their exotic characteristics. However, several issues have been encountered, such as the narrow bandwidth and inherent losses that restrict the spectrum and the variety of their applications. The losses have become the principal limiting factor when employing metamaterials in real-world applications. Consequently, overcoming them is crucially important and of practical necessity. This paper discusses the practical applications of metamaterials in constructing functional devices and the effects of the losses on such devices. In more depth, it reviews the available approaches for reducing the metamaterial losses developed over the last two decades in the light of available literature. These approaches include the utilization of the principles of electromagnetically induced transparency (EIT), geometric tailoring of the metamaterial structures, and embedding gain materials. Further, computational optimization techniques, such as particle swarm optimization (PSO) and genetic algorithm (GA), are also discussed to design low-loss metamaterials. The EIT-like metamaterial and the including of gain materials are systematic and universal approaches exhibiting low loss approaching zero. In contrast, the other two are not systematic and universal approaches.

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Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
IEEE Access no. 10, pages 67058 - 67080,
ISSN: 2169-3536
Language:
English
Publication year:
2022
Bibliographic description:
Esmail B., Kozieł S., Szczepański S., Majid H.: Overview of Approaches for Compensating Inherent Metamaterials Losses// IEEE Access -Vol. 10, (2022), s.67058-67080
DOI:
Digital Object Identifier (open in new tab) 10.1109/access.2022.3185637
Sources of funding:
Verified by:
Gdańsk University of Technology

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