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- W4243840663 abstract "Nanophotonics finds ever broadening applications requiring complex component designs with a large number of parameters to be simultaneously optimized. Recent methodologies employing optimization algorithms commonly focus on a single design objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. As a result, multiple performance criteria are clearly visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in how modern nanophotonic design is approached and provides a powerful tool to explore complexity in next-generation devices." @default.
- W4243840663 created "2022-05-12" @default.
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- W4243840663 date "2018-11-01" @default.
- W4243840663 modified "2023-09-27" @default.
- W4243840663 title "Mapping the global design space of nanophotonic components using machine learning pattern recognition" @default.
- W4243840663 doi "https://doi.org/10.31219/osf.io/xmnjs" @default.
- W4243840663 hasPublicationYear "2018" @default.
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