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A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires
1Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, People's Republic of China.
Nanotechnology
|February 2, 2022
Summary
This study introduces a novel method to accurately predict metal nanowire reflection properties for plasmonic devices. The approach merges physics and data to efficiently determine reflectivity and phase, aiding nanophotonic design.
Area of Science:
- Nanophotonics and Plasmonics
- Computational Physics
- Materials Science
Background:
- Metal nanowires are key components for advanced plasmonic devices.
- Accurate prediction of complex reflection coefficients is vital for device performance (resonance, lasing, sensing).
Purpose of the Study:
- To develop an efficient and reliable method for determining the reflectivity and reflection phase of metal nanowires.
- To transform the specific nanowire reflection problem into a universal regression problem.
Main Methods:
- Incorporation of physics-guided objective functions and constraints.
- Conversion of reflection problems to regression problems.
- Merging physics-based and data-driven modeling approaches.
Main Results:
- The proposed approach accurately determines reflectivity and reflection phase for metal nanowires.
- The method is effective for arbitrary geometry, environments, and terminal shapes.
- Demonstrated efficient and reliable prediction capabilities.
Conclusions:
- The developed method offers a valuable tool for nanophotonic design and theoretical studies.
- Facilitates the creation of comprehensive datasets for plasmonic architectures.
- Enables large-scale design and investigation of nanophotonic components.
