The atomic force microscope (AFM) is a versatile, high-resolution tool used to characterize

the topography and material properties of a large variety of specimens at nano-scale. The

interaction of the micro-cantilever tip with the specimen causes cantilever de

ections that are

measured by an optical sensing mechanism and subsequently utilized to construct the sample

topography. Recent years have seen increased interest in using the AFM to characterize soft

specimens like gels and live cells. This remains challenging due to the complex and competing

nature of tip-sample interaction forces (large tip-sample interaction force is necessary to achieve

favorable signal-to-noise ratios). However, large force tends to deform and destroy soft samples.

In situ estimation of the local tip-sample interaction force is needed to control the AFM cantilever

motion and prevent destruction of soft samples while maintaining a good signal-to-noise

ratio. This necessitates the ability to rapidly estimate the tip-sample forces from the cantilever

de

ection during operation. This work proposes a rst approach to a near real-time framework

for tip-sample force inversion. The inverse problem of extracting the tip-sample force as an

unconstrained optimization problem. A fast, parallel forward solver is developed by utilizing

graphical processing units (GPU). This forward solver shows an eective 30000 fold speed-up

over a comparable CPU implementation, resulting in milli-second calculation times. The forward

solver is coupled with a GPU based particle-swarm optimization implementation. The

proposed framework is demonstrated over a series of tip-sample interaction models of increasing

complexity. Most of these inversions are performed in sub-second timings, showing potential

for integration with on-line AFM imaging and material characterization.