In this work, we report on the integration of an atomic force microscope (AFM) into a helium ion microscope (HIM). The HIM is a powerful instrument, capable of imaging and machining of nanoscale structures with sub-nanometer resolution, while the AFM is a well-established versatile tool for multiparametric nanoscale characterization. Combining the two techniques opens the way for unprecedented in situ correlative analysis at the nanoscale. Nanomachining and analysis can be performed without contamination of the sample and environmental changes between processing steps. The practicality of the resulting tool lies in the complementarity of the two techniques. The AFM offers not only true 3D topography maps, something the HIM can only provide in an indirect way, but also allows for nanomechanical property mapping, as well as for electrical and magnetic characterization of the sample after focused ion beam materials modification with the HIM. The experimental setup is described and evaluated through a series of correlative experiments, demonstrating the feasibility of the integration.

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Nonlinear dynamics of piezo actuators such as hysteresis, distort the Atomic Force Microscopy (AFM) images as they adversely affect the accuracy of the nano-positioning setup. To compensate for the effects of hysteresis on lateral scanner actuators of AFM, a data-driven feedforward controller design algorithm is proposed. The pair of forward and backward images of a sample are used to extract a mapping between the trace and retrace motion of the actuator. A model corresponding to the input-output mapping of the actuator is defined with a set of unknown parameters. The values of these parameters, which shape the hysteresis curves of the actuator, are optimized through defining and solving an optimization problem. A genetic algorithm is utilized as a tool to look for the optimal values. The hysteresis mapping model is then implemented in the form of an inversion-based feedforward controller to correct the scan waveforms and get matching forward and backward images of the sample. The proposed sensor-less data-driven method is easy to implement as it does not depend on the instrument, the sample under study, or the imaging properties.

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