Mapping the global design space of nanophotonic components using machine learning pattern recognition.

ABSTRACT: Nanophotonics finds ever broadening applications requiring complex components with many parameters to be simultaneously designed. Recent methodologies employing optimization algorithms commonly focus on a single performance 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 desig

SUBMITTER: Melati D 

PROVIDER: S-EPMC6803653 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature