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<p>The precise calibration of indenter geometries is a critical factor in ensuring reliable instrumented indentation experiments, particularly at the microscale and nanoscale. This study presents the comprehensive and highly accurate calibration of a modified Berkovich indenter using a metrological large-range atomic force microscope, reaching a relative standard uncertainty of less than 1% for the projected area function (PAF) for indentation depths of 200&#xa0;nm and more. In contrast to previous works, this study specifically focuses on the influence of data evaluation methods on the determination of indenter geometry and the estimation of the measurement uncertainty. A refined uncertainty estimation approach is implemented, overcoming the limitations of both the classical “Guide to the Expression of Uncertainty in Measurement” and the Monte Carlo method, which either require restrictive idealization assumptions on the indenter geometry or entail high computational costs. By leveraging a systematic simulationbased uncertainty assessment, this work provides a robust evaluation of the PAF uncertainty and its dominant contributing factors. </p>

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Accurate AFM Calibration of Indenter Geometry for Instrumented Indentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月20日

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  • Johannes Degenhardt,
  • Xiukun Hu,
  • Mutaib Zackaria,
  • Febo Menelao,
  • Rainer Tutsch,
  • Gaoliang Dai

摘要

The precise calibration of indenter geometries is a critical factor in ensuring reliable instrumented indentation experiments, particularly at the microscale and nanoscale. This study presents the comprehensive and highly accurate calibration of a modified Berkovich indenter using a metrological large-range atomic force microscope, reaching a relative standard uncertainty of less than 1% for the projected area function (PAF) for indentation depths of 200 nm and more. In contrast to previous works, this study specifically focuses on the influence of data evaluation methods on the determination of indenter geometry and the estimation of the measurement uncertainty. A refined uncertainty estimation approach is implemented, overcoming the limitations of both the classical “Guide to the Expression of Uncertainty in Measurement” and the Monte Carlo method, which either require restrictive idealization assumptions on the indenter geometry or entail high computational costs. By leveraging a systematic simulationbased uncertainty assessment, this work provides a robust evaluation of the PAF uncertainty and its dominant contributing factors.