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Subpixel microscopic deformation analysis using correlation and artificial neural networks.

M Pitter, C W See, M Somekh

    Optics Express
    |May 7, 2009
    PubMed
    Summary
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    Digital image correlation combined with artificial neural networks (ANNs) accurately measures microscopic deformation. This approach enables fast, subpixel displacement analysis for microelectronic devices under stress.

    Area of Science:

    • Materials Science
    • Mechanical Engineering
    • Computer Science

    Background:

    • Microscopic deformation analysis is crucial for understanding material behavior.
    • Digital image correlation (DIC) is a common technique for measuring displacement.
    • Achieving subpixel accuracy in DIC can be challenging.

    Purpose of the Study:

    • To develop and evaluate a novel method for microscopic deformation analysis.
    • To achieve subpixel resolution in displacement measurements.
    • To assess the performance of artificial neural networks (ANNs) in conjunction with DIC.

    Main Methods:

    • Digital image correlation was employed to analyze images of deformed samples.
    • Artificial neural networks (ANNs) were trained to estimate subpixel displacements.

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  • Nonintegral pixel shifting was used to enhance resolution.

    • Main Results:

    • The combined DIC and ANN technique achieved subpixel accuracy in displacement measurement.
    • Results demonstrated comparable accuracy to existing subpixel methods.
    • ANNs facilitated rapid analysis without requiring the analytical form of the correlation function.

    Conclusions:

    • The integration of ANNs with DIC offers a fast and accurate method for microscopic deformation analysis.
    • This technique is suitable for analyzing microelectronic devices subjected to displacement and thermal stress.
    • ANNs provide a powerful tool for enhancing DIC analysis, reducing computational time.