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.

Experimental identification of a grating profile using neural network classifiers in optical scatterometry.

In this paper, we develop a new technique, to the best of our knowledge, of grating characterization based on two separate steps. First, an artificial neural network (ANN) is implemented in a classifier mode to identify the shape of the geometrical profile from a measured optical signature. Then, a second ANN is used in a regression mode to determine the geometrical parameters corresponding to the selected geometrical model.

Reconstruction of a complex profile shape by weighting basic characterization results for nanometrology.

Scatterometry has become an essential method of characterization during the fabrication process of nanostructures. This nondestructive technique based on diffracted light analysis is composed of two steps: the measurement of the optical signatures and the treatment to reconstruct the profile of the periodic structure. The artificial neural network has proved its effectiveness in solving the inverse scattering problem.