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Synthesis of coupled resonator optical waveguides by cavity aggregation
Pascual Muñoz1, José David Doménech, José Capmany
1Optical and Quantum Comm. Grp. - iTEAM - Universidad Politécnica de Valencia C/ Camino de Vera s/n - Valencia, Spain. pascual@ieee.org
Optics Express
|February 23, 2010
Summary
This study applies a layer aggregation method to coupled resonator optical waveguides, successfully extracting resonator coupling constants from the frequency transfer function. The algorithm
Area of Science:
- Photonics and Waveguide Technology
- Optical Engineering
- Resonator Systems
Background:
- Coupled resonator optical waveguides (CROWs) are fundamental components in integrated photonics.
- Accurate determination of coupling constants is crucial for designing and optimizing CROW devices.
- Existing methods for extracting coupling parameters can be complex or limited in scope.
Purpose of the Study:
- To introduce and validate the layer aggregation method for analyzing CROWs.
- To demonstrate the method's ability to derive coupling constants directly from the frequency transfer function.
- To investigate the convergence properties and parameter dependencies of the developed algorithm.
Main Methods:
- Application of the layer aggregation method to the theoretical model of coupled resonator optical waveguides.
- Derivation of coupling constants from the frequency transfer function of the waveguide system.
- Analysis of the convergence behavior of the developed numerical algorithm.
Main Results:
- The layer aggregation method successfully yields the coupling constants between resonators in CROWs.
- The frequency transfer function serves as a direct input for parameter extraction.
- The convergence of the algorithm was examined, and key parameters influencing it were identified and discussed.
Conclusions:
- The layer aggregation method provides an effective approach for characterizing coupling in CROWs.
- This technique offers a direct and potentially simplified route to obtaining critical device parameters.
- Further investigation into parameter optimization can enhance the method's applicability.
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