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The atomic force microscope (AFM) is a versatile, high-resolution tool usedto characterize the topography and material properties of a large variety ofspecimens at nano-scale. The interaction of the micro-cantilever tip with thespecimen causes cantilever deflections that are measured by an optical sensingmechanism and subsequently utilized to construct the sample topography. Recentyears have seen increased interest in using the AFM to characterize softspecimens like gels and live cells. This remains challenging due to the complexand competing nature of tip-sample interaction forces - large tip-sampleinteraction force is necessary to achieve good signal-to-noise ratios; However,large force tends to deform and destroy soft samples. In situ estimation of thelocal tip-sample interaction force is needed to control the AFM cantilevermotion and prevent destruction of soft samples while maintaining a goodsignal-to-noise ratio. This necessitates the ability to rapidly estimate thetip-sample forces from the cantilever deflection during operation. This paperproposes a first approach to a near real-time framework for tip-sample forceinversion. We pose the inverse problem of extracting the tip-sample force as anunconstrained optimization problem. A fast, parallel forward solver isdeveloped by utilizing graphical processing units (GPU). This forward solvershows an effective 30000 fold speed-up over a comparable CPU implementation,resulting in milli-second calculation times. The forward solver is coupled witha GPU based particle-swarm optimization implementation. We illustrate theframework on three classes of tip-sample interaction inversions. Each of theseinversions is performed in sub-second timings, showing potential forintegration with on-line AFM imaging and material characterization

A near real-time framework for extracting tip-sample forces in dynamic atomic force microscopy (dAFM)