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.

Effect of roughness on surface plasmons propagation along deep and shallow metallic diffraction gratings.

The roughness of shallow or deep metallic diffraction gratings modifies the propagation of surface plasmon mode along the metallic-air interface. The scattering losses lead to a spectral or angular broadening of the surface plasmon resonance (SPR) and to a shift of the resonance wavelength and coupling angle. This mechanism is deeply analyzed both experimentally and theoretically to overcome these effects when such structures, in particular deep ones, are used as SPR-based sensors.