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    This summary is machine-generated.

    Scatterometry, a noncontact optical metrology technique, faces challenges in solving inverse problems due to parameter correlations. This study introduces an algorithm to identify key parameters, simplifying complex optical metrology reconstructions.

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    Area of Science:

    • Optical Metrology
    • Computational Physics

    Background:

    • Scatterometry is a crucial nonimaging, noncontact optical metrology technique.
    • Inverse problems in scatterometry are complex due to inversion instability and local minima, especially with many parameters.
    • Parameter correlation significantly complicates accurate data inversion.

    Purpose of the Study:

    • To develop an algorithm for automatically identifying relevant parameters in scatterometry models.
    • To simplify the inverse problem by reducing model complexity.
    • To apply the algorithm to reconstruct 2D and 3D scatterers.

    Main Methods:

    • Proposed a novel algorithm for automatic parameter subset determination.
    • Applied the algorithm to the reconstruction of 2D and 3D scatterer models.
    • Compared the algorithm's performance against local sensitivity analysis and Morris's screening method.

    Main Results:

    • The developed algorithm effectively identifies the most relevant parameters for scatterometry models.
    • Successful application of the algorithm to reconstruct both 2D and 3D scatterer properties.
    • Demonstrated comparable or improved performance over existing methods for parameter selection.

    Conclusions:

    • The proposed algorithm offers an efficient approach to manage complexity in scatterometry inverse problems.
    • Automated parameter selection enhances the accuracy and reliability of scatterer reconstruction.
    • This method provides a valuable tool for advancing optical metrology applications.