The development of the spatially correlated adjustment wavelet filter for atomic force microscopy data

Area of Science:

• Materials Science
• Nanotechnology
• Data Analysis

Background:

• Atomic Force Microscopy (AFM) generates high-resolution surface data but is susceptible to artifacts.
• Existing artifact removal methods often require specialized expertise, limiting accessibility.
• The need for user-friendly and effective artifact reduction techniques in AFM is critical.

Purpose of the Study:

• To present a novel, practical approach for artifact removal in AFM measurements using 2D wavelet filtering.
• To enable easier adjustment of filtering parameters for average users.
• To demonstrate the effectiveness of the developed method on phase imaging and Electrostatic Force Microscopy (EFM) data.

Main Methods:

• Implementation of a 2D wavelet filter for artifact removal.
• Utilization of auxiliary data, such as photodiode signal maps, for targeted data processing.
• Development of a user-friendly function within the Gwyddion software package.
• Optimization of filtering parameters and verification of process performance.

Main Results:

• Successful artifact removal from AFM phase imaging and EFM measurement results.
• Demonstration of simplified parameter adjustment, making advanced filtering accessible to non-experts.
• Validation of the method's efficacy in improving the quality of AFM data.

Conclusions:

• The novel 2D wavelet filtering approach offers a practical and accessible solution for artifact removal in AFM.
• The integration with Gwyddion software and use of supplementary data enhance usability and performance.
• Wavelet filtering shows superior efficiency in removing local artifacts compared to 2D Fast Fourier Transform (FFT) based filters.