Towards enhanced nanoindentation by image recognition

The Oliver-Pharr method is maybe the most established method to determine a material's Young's modulus and hardness. However, this method has a number of requirements that render it more challenging for hard and stiff materials. Contact area and frame stiffness have to be calibrated for every tip, and the surface contact has to be accurately identified. The frame stiffness calibration is particularly prone to inaccuracies since it is easily affected, e.g., by sample mounting. In this study, we introduce a method to identify Young's modulus and hardness from nanoindentation without separate area function and frame stiffness calibrations and without surface contact identification. To this end, we employ automatic image recognition to determine the contact area that might be less than a square micrometer. We introduce the method and compare the results to those of the Oliver-Pharr method. Our approach will be demonstrated and evaluated for nanoindentation of Si, a hard and stiff material, which is challenging for the proposed method. Graphic

Automated summary

The Oliver-Pharr method is a traditional approach for determining a material's Young's modulus and hardness, but it poses challenges for hard and stiff materials. This study introduces a new method that utilizes automatic image recognition to accurately identify Young's modulus and hardness from nanoindentation, eliminating the need for separate calibrations and surface contact identification. Our approach is demonstrated and evaluated for challenging nanoindentation of hard and stiff materials like silicon.