ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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. Usually the scatterometry characterization process requires a previous geometrical profile shape to be defined. This study proposes a method for the profile reconstruction of any geometrical shape, not necessarily one that is included in the initial model. For example, this could include the detection or the identification of an incorrect profile shape in a lithography or etching process. This so-called weighting profile approach consists in combining several results of basic characterizations for the reconstruction of the real profile shape. In this study, the feasibility of this method is demonstrated on theoretical samples to satisfy the requirements of scatterometry. Finally, the weighting profile method is compared with a classical scatterometry method involving a generic profile shape.
ABSTRACT
We have previously shown that ISG20, an interferon (IFN)-induced gene, encodes a 3' to 5' exoribonuclease member of the DEDD superfamily of exonucleases. ISG20 specifically degrades single-stranded RNA. In this report, using immunofluorescence analysis, we demonstrate that in addition to a diffuse cytoplasmic and nucleoplasmic localization, the endogenous ISG20 protein was present in the nucleus both in the nucleolus and in the Cajal bodies (CBs). In addition, we show that the ectopic expression of the CBs signature protein, coilin, fused to the red fluorescent protein (coilin-dsRed) increased the number of nuclear dots containing both ISG20 and coilin-dsRed. Using electron microcopy analysis, ISG20 appeared principally concentrated in the dense fibrillar component of the nucleolus, the major site for rRNA processing. We also present evidences that ISG20 was associated with survival of motor neuron (SMN)-containing macromolecular nuclear complexes required for the biogenesis of various small nuclear ribonucleoproteins. Finally, we demonstrate that ISG20 was associated with U1 and U2 snRNAs, and U3 snoRNA. The accumulation of ISG20 in the CBs after IFN treatment strongly suggests its involvement in a new route for IFN-mediated inhibition of protein synthesis by modulating snRNA and rRNA maturation.
