Reconstruction of surface potential from Kelvin probe force microscopy images

Area of Science:

• Surface science
• Scanning probe microscopy
• Materials characterization

Background:

• Kelvin probe force microscopy (KPFM) measures surface potential but is affected by tip-sample interactions.
• KPFM images represent a weighted average of the true surface potential due to electrostatic forces.
• Amplitude modulation KPFM (AM-KPFM) and frequency modulation KPFM (FM-KPFM) are common KPFM techniques with different sensitivities.

Purpose of the Study:

• To develop and validate an algorithm for reconstructing accurate sample surface potential from KPFM images.
• To quantify and correct for the averaging effect in KPFM measurements.
• To compare the performance of the reconstruction algorithm in AM-KPFM and FM-KPFM.

Main Methods:

• Modeling KPFM measurements as a linear shift-invariant system using a point spread function (PSF).
• Calculating the KPFM probe's PSF and incorporating measured noise statistics.
• Deconvolving KPFM images to recover the true surface potential.
• Applying the algorithm to CdS-PbS nanorods (AM-KPFM) and graphene (FM-KPFM).

Main Results:

• The developed algorithm successfully reconstructs surface potential from KPFM data.
• Substantial averaging effects were observed in AM-KPFM measurements of nanorods.
• Negligible averaging effects were found in FM-KPFM measurements of graphene.
• The point spread function (PSF) accurately characterizes the KPFM measurement system.

Conclusions:

• The deconvolution algorithm provides accurate surface potential reconstruction from KPFM.
• FM-KPFM significantly reduces the averaging effect compared to AM-KPFM.
• This method enhances the reliability of surface potential mapping in nanoscale materials.