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Topological Learning for the Classification of Disorder: An Application to the Design of Metasurfaces
Tristan Madeleine1, Nina Podoliak2, Oleksandr Buchnev3
1Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.
ACS Nano
|December 18, 2023
Summary
Structural disorder enhances metasurface optical properties. New topological descriptors quantify disorder, aiding the design and fabrication of improved plasmonic metasurfaces for better light extraction.
Area of Science:
- Materials Science
- Nanotechnology
- Optics
Background:
- Structural disorder in metasurfaces can enhance optical properties like light extraction.
- Correlated disorder, arising from fabrication or design, is particularly effective.
Purpose of the Study:
- To introduce novel numerical descriptors based on topology for quantifying disorder in metasurfaces.
- To enable accurate measurement of both correlated and uncorrelated disorder across all length scales.
Main Methods:
- Developing and applying topological descriptors to measure disorder.
- Designing plasmonic metasurfaces with controlled disorder.
- Correlating disorder strength with surface lattice resonance properties.
Main Results:
- Demonstrated the accuracy of topological descriptors theoretically and experimentally.
- Successfully designed plasmonic metasurfaces with tailored disorder.
- Established a correlation between disorder quantification and surface lattice resonance strength.
Conclusions:
- Topological descriptors offer a universal method for quantifying disorder in nanostructures.
- These descriptors facilitate rapid and precise design of disordered metasurfaces.
- The tools can aid in optimizing fabrication processes for enhanced optical performance.
Keywords:
designdisordermetasurfaceoptimizationplasmonicsurface lattice resonancetopological data analysisMore Related Videos
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