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  1. Investigating surface structures by EUV scattering

    An exploration of soft X-ray and EUV-scatterometry from grazing to near normal incidence is presented. Measurements are performed on e-beam written silicon gratings. The reconstructed geometrical line shape models are statistically validated by applying a Markov-Chain Monte Carlo sampling technique. Experimental data and simulation results provide insight into the potential of EUV scatterometry.

    V. Soltwisch, et al. Investigating surface structures by EUV scattering. Proc. SPIE 10143, 101430P (2017).

    2017 DOI

    Optical Metrology and Sensing, Optical and EUV Lithography, Advanced Finite Element Methods, Optimization and Parameter Retrieval Methods, Uncertainty Quantification Methods

  2. Metrology of nanoscale grating structures by UV scatterometry

    Goniometric scatterometry measurements of gratings with linewidths down to 25 nm on silicon wafers with an inspection wavelength of 266 nm are presented. Data evaluation is performed using FEM based light scattering simulations. As results the reconstruction of the complete cross-section profile is presented.

    M. Wurm, et al. Metrology of nanoscale grating structures by UV scatterometry. Opt. Express 25, 2460 (2017).

    2017 DOI Publication link

    Optical Metrology and Sensing, Light Scattering Computation, Optimization and Parameter Retrieval Methods

  3. Quantifying parameter uncertainties in optical scatterometry using Bayesian inversion

    A Newton-like method is presented to solve inverse problems and to quantify parameter uncertainties. FEM, including direct computation of partial derivatives, is used to solve the forward-problem.

    M. Hammerschmidt, et al. Quantifying parameter uncertainties in optical scatterometry using Bayesian inversion. Proc. SPIE 10330, 1033004 (2017).

    2017 DOI Publication link

    Optical Metrology and Sensing, other fields, Advanced Finite Element Methods, Light Scattering Computation, Optimization and Parameter Retrieval Methods, Uncertainty Quantification Methods

  4. Quantitative optical imaging for in-die-capable critical dimension targets

    FEM simulations are used in a work by U.S. National Institute of Standards and Technology to optimize the design of in-die-capable metrology targets for process control in microlithography.

    B. M. Barnes, et al. Enabling quantitative optical imaging for in-die-capable critical dimension targets. Proc. SPIE 9778, 97780Y (2016).

    2016 DOI

    Optical Metrology and Sensing, Optical and EUV Lithography, Advanced Finite Element Methods, Optimization and Parameter Retrieval Methods

  5. Fiber grating couplers on SOI

    JCMsuite is used to design fiber grating couplers on SOI for high coupling efficiency.

    B. Wohlfeil, et al. Optimization of fiber grating couplers on SOI using advanced search algorithms. Opt. Lett. 39, 3201 (2014).

    2014 DOI

    diffractive optics, integrated optics, Optimization and Parameter Retrieval Methods

  6. Efficient Bayesian inversion for shape reconstruction of lithography masks

    In order to quantify the uncertainties of reconstructed geometry parameters, a fast-to-evaluate surrogate for the forward model (a polynomial chaos expansion) is introduced. The surrogate allows, e.g., for determining the probability distribution of the geometry parameters given measurement data, and for a global sensitivity analysis of the measurement process. All methods are implemented in JCMsuite's analysis and optimization toolbox.

    N. Frachmin, et al. Efficient Bayesian inversion for shape reconstruction of lithography masks. Journal of Micro/Nanolithography, MEMS, and MOEMS, 19(2), 024001 (2020).

    2010 DOI Publication link

    Optical Metrology and Sensing, Optical and EUV Lithography, Light Scattering Computation, Optimization and Parameter Retrieval Methods, Uncertainty Quantification Methods