Abstract
In our study, we investigate the resonance modes of plasmonic nanodisks through numerical simulations and theoretical analysis. These tiny structures exhibit fascinating behavior, but relying solely on mode localization is not sufficient to classify their supported modes as plasmonic or dielectric. Our goal is to address this challenge by introducing a robust method for identifying each mode’s true nature. Moreover, through analysis of the field distribution, we introduce, to our knowledge, a novel metric designed for application in inverse problems within the realm of machine learning. This metric serves as a robust tool for optimizing the performance of photonic devices.
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