SEM-based spatial analysis of rough surfaces: Topographic edge effects and their mitigation.

Area of Science:

• Surface science
• Materials characterization
• Nanotechnology

Background:

• Nanostructured surfaces are crucial in diverse fields like semiconductors and medical diagnostics.
• Scanning Electron Microscopy (SEM) is vital for high-resolution surface imaging.
• SEM image analysis offers insights into surface spatial arrangements, but edge effects pose challenges.

Purpose of the Study:

• To investigate how SEM edge effects impact spatial statistics of rough, step-like surfaces.
• To demonstrate the distortion of metrics like Fourier spectrum and correlation function.
• To develop and validate a method for mitigating SEM edge effects.

Main Methods:

• Analysis of SEM edge effects on spatial statistics metrics (Fourier spectrum, correlation function).
• Simulation of rough step-like surfaces and SEM imaging.
• Development of a pixel-averaging method to correct edge effects.
• Application and validation of the method on simulated and real SEM images.

Main Results:

• SEM edge effects significantly distort widely used spatial statistics metrics.
• The width of edges critically influences the impact on surface spatial statistics.
• The proposed pixel-averaging method effectively mitigates edge effects.
• The method successfully restored original spatial statistics for both simulated and real SEM data.

Conclusions:

• SEM edge effects can lead to misinterpretations of nanostructured surface topography.
• Accurate spatial statistics analysis requires addressing these edge effects.
• The developed averaging method provides a reliable approach to correct SEM images of rough surfaces.
• This technique enhances the utility of SEM for quantitative surface characterization.