RESUMO
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. The advantage of this approach is highlighted by discussions and studies involving the error criterion that is used widely in scatterometry. In addition, experimental tests are provided on diffraction grating structures with a period of 500 and 750 nm.
RESUMO
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. From the opto-geometrical parameters such as period, depth and shape of the pattern determined by numerical analysis, these structures have been fabricated using controlled slope plasma etching processes. In order to extend the antireflective properties up to the visible wavelengths, patterned-topped cones have been fabricated as well. Afterwards, optical characterizations of several samples were carried out. Thus, the performances of the flat-top and patterned-topped cones have been compared in the visible and mid infrared range.