Surface topography analysis with application of roughness area dependence method.

Area of Science:

• Materials Science
• Surface Science
• Nanotechnology

Background:

• Root mean square roughness (R_q) is a standard but limited parameter for surface characterization.
• R_q uses only height variations, providing a single, often insufficient, descriptor of surface properties.

Purpose of the Study:

• To develop an advanced surface analysis method overcoming R_q limitations.
• To extract more comprehensive surface property information using roughness dependence on sampling area.
• To investigate the impact of growth parameters on semiconductor surfaces.

Main Methods:

• Developed a method calculating roughness as a function of increasing sampling area dimensions.
• Utilized Atomic Force Microscopy (AFM) to obtain surface topography data.
• Analyzed roughness area dependence plots for Gallium Nitride (GaN) and Aluminum Gallium Nitride/Gallium Nitride (AlGaN/GaN) heterostructures.

Main Results:

• Demonstrated that R_q dependence on sampling area provides richer surface information than a single R_q value.
• Successfully characterized surface properties of GaN and AlGaN/GaN heterostructures grown via Metal Organic Chemical Vapor Deposition (MOVPE).
• Showcased the method's applicability across different substrates (sapphire, silicon) and scale ranges.

Conclusions:

• The proposed surface analysis method offers a more detailed understanding of surface properties.
• This technique enhances the characterization of semiconductor materials and heterostructures.
• The method is versatile and applicable to various topography imaging techniques and scale ranges.