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.

Flat-top and patterned-topped cone gratings for visible and mid-infrared antireflective properties.

Achieving a broadband antireflection property from material surfaces is one of the highest priorities for those who want to improve the efficiency of solar cells or the sensitivity of photo-detectors. To lower the reflectance of a surface, we are concerned with the study of the optical response of flat-top and patterned-topped cone shaped silicon gratings, based on previous work exploring pyramid gratings. Through rigorous numerical methods such as Finite Different Time Domain, we first designed several flat-top structures that theoretically demonstrate an antireflective character within the middle infrared region.