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

TL;DR

This paper introduces a GPU-accelerated, near real-time framework for estimating tip-sample forces in dynamic atomic force microscopy, enabling rapid force inversion during AFM operation to protect soft samples.

Contribution

It presents the first near real-time force inversion framework for dAFM using GPU acceleration and particle-swarm optimization, significantly speeding up calculations.

Findings

Achieved 30,000-fold speed-up with GPU-based forward solver

Performed force inversion in sub-second times

Demonstrated potential for real-time AFM imaging and characterization

Abstract

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 deflections 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 good 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 deflection during operation. This paper proposes a first approach to a near real-time framework for tip-sample force inversion. We pose 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 effective 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. We illustrate the framework on three classes of tip-sample interaction inversions. Each of these inversions is performed in sub-second timings, showing potential for integration with on-line AFM imaging and material characterization