Rapid broadband discrete nanomechanical mapping of soft samples on atomic force microscope

In this paper, an approach to achieve rapid broadband discrete nanomechanical mapping of soft samples using an atomic force microscope is developed. Nanomechanical mapping (NM) is needed to investigate, for example, dynamic evolution of the nanomechanical distribution of the sample-provided that the mapping is fast enough. The throughput of conventional NM methods, however, is inherently limited by the continuous scanning involved where the probe visits each sampling location continuously. Thus, we propose to significantly reduce the number of measurements through discrete mapping where only discrete sampling locations of interests are visited and measured. An online-searching learning-based technique is utilized to achieve rapid probe engagement and withdrawal with the interaction force minimized at each sampling location. Then, a control-based nanoindentation measurement technique is used to quickly acquire the nanomechanical property at each location, over frequencies that can be chosen arbitrarily in a broad range. Finally, a decomposition-based learning approach is explored to achieve rapid probe transitions between the sampling locations. The proposed technique is demonstrated through experiments using a Polydimethylsiloxane (PDMS) sample and a PDMS-epoxy sample as examples.