Identification of waveguide mode and surface plasmon resonance mode using Fourier cross-correlation analysis.

Abstract

The light reflected into the back focal plane of a microscope objective allows one to gather a great deal of information about the resonant modes excited on a sample. These dips represent modes excited on the sample, which are related to both the material properties and the structure. Automatic identification of these resonances is a vital stage in developing automated machine-learning techniques for high-throughput sample characterization. In previous work, identification of a single isolated mode was demonstrated; here we show how multiple modes can be separately identified using an automated centering procedure in a process we call radial thresholding. Once the center was determined, the radial thresholding process was modified and combined with interpolation to locate the precise modal positions. We show that this method is capable of resolving very closely spaced modes and is sensitive to nanometric changes in sample dimensions. The processing time for the method is sufficiently fast to ensure that it is suited for rapid sample identification.