Abstract
A frequency selective surface (FSS) is a periodic array of conductive elements (located on a dielectric substrate) that has a specific transmission or reflection response when illuminated with electromagnetic energy. Since the FSS response is sensitive to changes in substrate properties (permittivity, loss tangent, and thickness) as well as element geometry, FSS-based sensors have strong potential as a wireless sensing solution. To this end, this paper proposes three performance metrics (resonant frequency, resonant depth, and quality factor) to quantify the effect of substrate properties on sensor performance. These metrics are applied to a patch- A nd loop-based FSS sensor. The results show that the patch-based sensor has improved performance when designed using a thinner and high loss substrate, while the loop-based sensor has better performance when a substrate with less loss, but increased thickness is used. Additionally, measurements are also provided for sensors utilizing the low loss substrate. Finally, the performance of the two sensors is shown for strain sensing. The results indicate that the resonant frequency shift (when under mechanical loading) of both sensors is largely independent of the substrate permittivity.
Recommended Citation
M. Mahmoodi and K. M. Donnell, "Performance Metrics for Frequency Selective Surface-Based Sensors," IEEE Sensors Letters, vol. 1, no. 6, article no. 8114246, Institute of Electrical and Electronics Engineers, Dec 2017.
The definitive version is available at https://doi.org/10.1109/LSENS.2017.2774830
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
dielectric properties; frequency selective surface (FSS); FSS-based sensing; Microwave/millimeter wave sensors; performance metrics; strain sensing
International Standard Serial Number (ISSN)
2475-1472
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2017