Quartz tuning fork based three-dimensional topography imaging for sidewall with blind features

Area of Science:

• Materials Science
• Nanotechnology
• Semiconductor Physics

Background:

• Atomic force microscopy (AFM) is crucial for 3D imaging, especially in semiconductor manufacturing.
• Current 3D-AFM techniques struggle with long scan times and instability due to algorithmic limitations.
• Imaging blind features and sharp nanostructures presents significant challenges.

Purpose of the Study:

• To develop an improved 3D-AFM algorithm for efficient and stable imaging of complex 3D nanostructures.
• To overcome the limitations of existing 3D-AFM scanning strategies, including long scan times and operational instability.
• To enable high-resolution 3D imaging of challenging samples like silicon pillars and ZnO nano-rods.

Main Methods:

• An intelligent 3D scanning algorithm was developed, incorporating sidewall history tracking.
• The algorithm includes troubleshooting mechanisms for sharp sidewalls and probe sticking.
• Reactive direction adjustment was implemented to optimize the scanning path.

Main Results:

• The improved algorithm successfully achieved 3D imagery of ZnO nano-rods and silicon nano-pillars.
• High aspect-ratio multiwall carbon nanotube-based AFM probes were utilized.
• The method demonstrated reduced scanning time without compromising image quality or stability.

Conclusions:

• The developed intelligent 3D-AFM algorithm significantly enhances imaging speed and operational stability.
• This technique provides a reliable method for constructing 3D images of arbitrary shapes, particularly in semiconductor applications.
• The study offers a valuable advancement for nanoscale metrology and characterization.