Fourier-reconstructed force fingerprints in AFM: machine learning for novel contrast

G. Haugstad, A. Avery, R. Rahn, S. Hubig, B. Luo, H.-S. Lee, A. McCormick, D. Forschheimer
University of Minnesota,
United States

Keywords: microscopy, surfactant, thin film, nanoparticle


On surfactant-based films pertinent to lubrication and superhydrophilicity, dynamic force methods are used to sense both the elastic and viscous response to an atomic force microscopy (AFM) tip. Specifically a new type of "multifrequency" AFM -- vibrating the microcantilever (to which the tip is attached) at two tones near the fundamental flexural resonance -- generates dozens of tones of response due to a nonlinear tip-sample interaction (a well-known concept in electrical engineering, termed intermodulation). This method both expands contrast mechanisms (images of amplitude and phase at each of 40 mixing tones) and the ability to reconstruct the distance dependence of conservative and dissipative material response at each image pixel (via a 40-term discrete Fourier transform of tip motion per location). Machine learning is further applied to cluster-analyze these distance-dependent force fingerprints and thereby improve signal/noise to generate more sharply differentiated film domains, an entirely new concept in contrast improvements.