Generalized Maxwell projections for multi-mode network Photonics.
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
PRIMALIGHT Research Group
PRIMALIGHT, Faculty of Electrical Engineering, Applied Mathematics and Computational Sci-4ence, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.
KAUST Grant Number
OSR-2016-CRG5-2995Date
2020-06-03Preprint Posting Date
2020-02-19Online Publication Date
2020-06-03Print Publication Date
2020-12Submitted Date
2020-02-20Permanent link to this record
http://hdl.handle.net/10754/661771
Metadata
Show full item recordAbstract
The design of optical resonant systems for controlling light at the nanoscale is an exciting field of research in nanophotonics. While describing the dynamics of few resonances is a relatively well understood problem, controlling the behavior of systems with many overlapping states is considerably more difficult. In this work, we use the theory of generalized operators to formulate an exact form of spatio-temporal coupled mode theory, which retains the simplicity of traditional coupled mode theory developed for optical waveguides. We developed a fast computational method that extracts all the characteristics of optical resonators, including the full density of states, the modes quality factors, and the mode resonances and linewidths, by employing a single first principle simulation. This approach can facilitate the analytical and numerical study of complex dynamics arising from the interactions of many overlapping resonances, defined in ensembles of resonators of any geometrical shape and in materials with arbitrary responses.Citation
Makarenko, M., Burguete-Lopez, A., Getman, F., & Fratalocchi, A. (2020). Generalized Maxwell projections for multi-mode network Photonics. Scientific Reports, 10(1). doi:10.1038/s41598-020-65293-6Sponsors
Te authors acknowledge support from KAUST (OSR-2016-CRG5-2995) and Shaheen supercomputer from the Kaust Supercomputing Laboratory (KSL).Publisher
Springer NatureJournal
Scientific ReportsPubMed ID
32493942PubMed Central ID
PMC7270083arXiv
2002.08121Additional Links
http://www.nature.com/articles/s41598-020-65293-6ae974a485f413a2113503eed53cd6c53
10.1038/s41598-020-65293-6
Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific Reports
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