RESUMO
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. This decision tool operates in classifier mode prior to further processing. Then, the geometrical parameters of the structure can be reliably determined if necessary. Two solutions can be considered: the detection of a fixed geometrical shape operating on a binary mode and the identification of a geometrical shape from a limited number of profiles. These methods are validated in the context of plasmonic sensors on experimental sinusoidal grating structures with a grating period of 627 nm.
RESUMO
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. The zeroth and -1st orders exhibit two crossing angles enabling high-sensitivity common-mode detection schemes designed to reject variations in source power and environmental noise. The proof of concept and experimental assessment of this new surface plasmon resonance (SPR) sensing scheme are demonstrated by monitoring gases in a pressure-controlled chamber. A limit of detection (LOD) of 2 × 10-7 refractive index unit (RIU) was achieved.
