Characterization of diffraction gratings in a rigorous domain with optical scatterometry: hierarchical neural-network model

Characterization of microstructures with features from submicrometers to hundreds of micrometers requires versatile methods. Profilometry and optical microscopy cannot cope with submicrometer features, and atomic-force microscopy, scanning-electron microscopy, and near-field microscopy are inherently slow, off-line methods. In optical scatterometry, the laser light scattered from a sample is measured and the sample profile is subsequently characterized. We propose the use of a two-stage model based on neural networks: rough categorization followed by refinement, thus reducing the need for prior information on the sample. We simulate the method for a submicrometer diffraction grating characterized by five parameters. It is shown that intensity measurements of few diffraction orders by use only of one wavelength are enough to yield rms errors of less than 2 nm for the parameters (approximately 2–3% of the optimal values of the parameters).