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

This paper presents a post-filtering approach to eliminate distortions in atomic force microscope (AFM) images caused by acoustic noise from an unknown location. AFM operations are sensitive to external disturbances including acoustic noise, as disturbances 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. Advanced online control techniques face difficulty in capturing the complex noise dynamic and limited system bandwidth imposed by robustness requirement. In this work, we propose a dynamics-based optimal filtering technique to remove the acoustic-caused distortions in AFM images. A dictionary-approach is integrated with time-delay measurement to localize the noise source and estimate the corresponding acoustic dynamics. Then a noise-to-image coherence minimization approach is proposed to minimize the acoustic-caused image distortion via a gradient-based optimization to seek an optimal modulator to the acoustic dynamics. Finally, the filter is obtained as the finite-impulse response of the optimized acoustic dynamics. Experimental implementation is presented and discussed to illustrate the proposed technique.