Accurate detection of subsurface microcavity by bimodal atomic force microscopy

Area of Science:

• Materials Science
• Nanotechnology
• Surface Science

Background:

• Atomic Force Microscopy (AFM) is a powerful tool for surface imaging.
• Bimodal AFM utilizes multiple eigenmodes for enhanced sensitivity.
• Subsurface imaging presents challenges due to signal attenuation and interference.

Purpose of the Study:

• To investigate the subsurface detection capability of bimodal AFM.
• To identify critical parameters affecting image contrast for buried microcavities.
• To establish guidelines for accurate subsurface structure characterization.

Main Methods:

• Utilized a buried microcavity in a highly oriented pyrolytic graphite (HOPG) flake on a CD-R substrate as a reference sample.
• Employed bimodal AFM to analyze image contrast variations.
• Performed theoretical and experimental investigations of parameter effects.

Main Results:

• Image contrast is significantly influenced by second eigenmode amplitude and frequency, as well as sample properties.
• Improper parameter settings can lead to reduced, reversed, or absent contrast, hindering microcavity identification.
• Accurate detection requires minimizing second eigenmode amplitude (while maintaining signal-to-noise ratio) and matching second eigenmode frequency to the cantilever's resonance frequency.

Conclusions:

• Bimodal AFM's subsurface detection capability is sensitive to parameter selection and sample characteristics.
• Optimal parameter settings are essential for reliable identification of buried microcavities.
• Findings facilitate bimodal AFM applications in nano-characterization of subsurface structures like micro-/nano-channels.