Characterization of a perfect sinusoidal grating profile using an artificial neural network for plasmonic-based sensors.

In this paper, we present a system intended to detect a targeted perfect sinusoidal profile of a diffraction grating during its manufactured process. Indeed, the sinusoidal nature of the periodic structure is essential to ensure optimal efficiency of specific applications as plasmonic sensors (surface plasmon resonance -based sensors). A neural network is implemented to characterize the geometrical shape of the structure under testing at the end of the laser interference lithography process.

Common-mode plasmon sensing scheme as a high-sensitivity compact SPR sensor.

A deep metal grating enables quasi-phase-matched simultaneous excitation of two counterpropagating surface plasmon modes by means of its +1st and -2nd diffraction orders. The resulting angular reflection spectra of the scattered -1st and zeroth orders exhibit three interleaved zeros and maxima in a range centered around the Littrow angle. The spectra differ thoroughly from the usual reflection dip resulting from single-order plasmon coupling that produces strong absorption.