Data-driven dynamics-based optimal filtering of acoustic noise at arbitrary location in atomic force microscope...

Area of Science:

• Physics
• Materials Science
• Nanotechnology

Background:

• Atomic Force Microscopy (AFM) is highly sensitive to external acoustic noise, leading to image distortions.
• Conventional noise cancellation methods in AFM have limitations and cannot fully eliminate residual noise.
• Advanced control techniques struggle with complex noise dynamics and system bandwidth constraints.

Purpose of the Study:

• To develop a post-filtering approach for eliminating acoustic noise-induced distortions in AFM images.
• To propose a dynamics-based optimal filtering technique for improved AFM image quality.
• To address the limitations of existing noise reduction methods in AFM.

Main Methods:

• A dictionary-based approach combined with time-delay measurement to localize noise sources and estimate acoustic dynamics.
• A noise-to-image coherence minimization strategy using gradient-based optimization to reduce acoustic-induced image distortion.
• Derivation of the optimal filter as the finite-impulse response of the optimized acoustic dynamics.

Main Results:

• Successful localization of unknown acoustic noise sources.
• Estimation of corresponding acoustic dynamics.
• Minimization of acoustic-induced image distortions through an optimized filter.
• Experimental validation of the proposed post-filtering technique.

Conclusions:

• The proposed dynamics-based optimal filtering technique effectively removes acoustic noise distortions in AFM images.
• The integration of dictionary approach, time-delay measurement, and coherence minimization offers a robust solution.
• This method provides a significant improvement over conventional noise cancellation techniques for AFM imaging.