J
Jyrki Saarinen
Researcher at Helsinki University of Technology
Publications - 31
Citations - 366
Jyrki Saarinen is an academic researcher from Helsinki University of Technology. The author has contributed to research in topics: Diffraction efficiency & Diffraction grating. The author has an hindex of 11, co-authored 31 publications receiving 361 citations. Previous affiliations of Jyrki Saarinen include Nokia.
Papers
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Guided-mode resonance filters of finite aperture
TL;DR: In this article, the authors investigate the properties of finite-aperture waveguide-grating resonance filters by means of rigorous electromagnetic theory and an approximate model, and provide an optical-engineering model for the estimation of the minimum grating size required to achieve a high resonance-wavelength reflectivity.
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Optical scatterometry of subwavelength diffraction gratings: neural-network approach
TL;DR: The use of a neural network is demonstrated as a promising method for performing an accurate quantitative characterization of the geometry of a diffraction grating with subwavelength grooves.
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Simulation of field-assisted ion exchange for glass channel waveguide fabrication: effect of nonhomogeneous time-dependent electric conductivity
TL;DR: In this paper, the influence of the nonhomogeneous conductivity on a buried channel waveguide is studied in detail, and it is shown that there is a substantial variation of electric current density during the process, particularly in the waveguide region.
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Double-ion-exchange process in glass for the fabrication of computer-generated waveguide holograms
TL;DR: This work experimentally optimize double-ion-exchange process parameters to achieve a designed phase modulation for a wave front passing through a computer-generated waveguide hologram and numerically analyze the effects of fabrication errors.
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Characterization of diffraction gratings in a rigorous domain with optical scatterometry: hierarchical neural-network model.
TL;DR: This work proposes 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, and shows that intensity measurements of few diffraction orders by use only of one wavelength are enough to yield rms errors of less than 2 nm.