Simulated Data for Comparison of On-chip Terahertz Filters for Sub-wavelength Dielectric Sensing

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Authors: Joshua P. R. Nixon, C. D. W. Mosley, SaeJune Park, Wood, Christopher, John Cunningham
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Abstract

This paper discusses the application of on-chip terahertz (THz) filters attached to waveguides that can act as sensor elements, including for scanned imaging applications. Our work presents a comparative numerical study of several different geometries (comprising five split-ring resonator geometries and a quarter-wavelength stub resonator, the latter being well established as a sensor at THz frequencies and therefore able to act as a benchmark. We designed each structure to have a resonant frequency of 500 GHz, allowing the impact of resonator geometry on sensing performance to be isolated; the performance was quantified by assessing each design using four figures of merit: resonance quality factor, sensitivity (relative frequency shift under dielectric loading), responsivity (sensitivity weighted by resonance sharpness), and the electric field confinement area. Simulations were conducted using Ansys HFSS using the properties of commercially available photoresist (Shipley 1813) as a dielectric load to assess performance under conditions comparable to previous experimental studies. The analysis showed that while sensitivity remained broadly similar across geometries, responsivity and quality factor differed substantially between resonators. Furthermore, the spatial distribution of the electric field and current density, particularly in rotated configurations, was found to significantly impact coupling efficiency between the resonator and transmission line. Our findings provide guidance for the general design of systems employing THz sensors while establishing a framework with which to benchmark future sensor geometries.

Fields of Study & Subjects

Terahertz technology and applications Microwave and Dielectric Measurement Techniques Metamaterials and Metasurfaces Applications

Publication details

Journal / source
University of Leeds
Publication date
24 February 2026
Language
EN
Document type
Dataset