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Updated: Jun 14, 2026

Fabrication and Characterization of Superconducting Resonators
Published on: May 21, 2016
Extracting coupling and loss coefficients from a ring resonator
W R McKinnon1, D X Xu, C Storey
1Institute for Microstructural Sciences, National Research Council of Canada, 1200 Montreal Road, Ottawa, Ontario, Canada. ross.mckinnon@nrc-cnrc.gc.ca
A new method extracts coupling and loss coefficients from single ring resonators. These optical coefficients can be distinguished by analyzing their changes with wavelength or device parameters.
Area of Science:
- Photonics and Optical Engineering
- Integrated Optics
- Resonator Devices
Background:
- Ring resonators are fundamental components in integrated photonics.
- Accurate characterization of coupling and loss coefficients is crucial for device design and performance optimization.
- Existing methods may require multiple resonators or complex setups.
Purpose of the Study:
- To develop a novel method for extracting optical coupling and loss coefficients from a single ring resonator.
- To provide a simplified approach for characterizing resonator performance.
- To enable disentanglement of coupling and loss parameters from resonance spectra.
Main Methods:
- Analysis of resonance peak widths, depths, and spacings within a single ring resonator.
- Application of derived formulas to extract characteristic coefficients.
- Investigation of the wavelength and device parameter dependence of the coefficients to distinguish between coupling and loss.
Main Results:
- Successfully extracted coupling and loss coefficients from single resonator spectra.
- Demonstrated a method to differentiate between coupling and loss coefficients.
- Validated the technique's reliance on spectral variations with wavelength or device parameters.
Conclusions:
- The developed method offers an efficient way to determine critical optical parameters of ring resonators.
- This technique simplifies the characterization process, requiring only a single resonator.
- Understanding the disentanglement based on spectral variations is key for accurate coefficient assignment.
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