Data-Driven Robust Acoustic Noise Filtering for Atomic Force Microscope Image

This article proposes a data-driven acoustic vibration filtering technique to eliminate acoustic-caused distortions in atomic force microscope (AFM) images. AFM measurement is sensitive to external disturbances including acoustic noises, as disturbance to the probe–sample interaction directly results in distortions in the sample images obtained. Although conventional passive noise cancellation has been employed, limitation exists and residual noise still persists. The acoustic dynamics involved is complicated, broadband, and not decaying with frequency increase. More challenge arises in practice as the location of the acoustic noise source tends to be unknown and arbitrary, resulting in low signal to noise ratio (SNR) in the acoustic signal measurement, and large error in the acoustic dynamics quantified. In this work, we propose a Wiener-filter-based robust filtering technique to improve both the SNR of the acoustic signal measured and reduce the error in the acoustic dynamics obtained. Then, a coherence minimization approach is proposed to further enhance the accuracy of the filter without modeling. Experimental implementation is presented and discussed to illustrate the proposed technique.