Abstract
High-speed atomic force microscopy (HS-AFM) has evolved out of substantial developments
to the imaging rates of atomic force microscopy (AFM), largely to improve its ability to
view dynamic events in situ, bringing imaging times for frames to under a second. Typically,
many state-of-the-art HS-AFMs compromise image resolution and/or frame size to achieve
such speeds to view these dynamics.
A commercially available HS-AFM, capable of a very high data rate of 2 MHz, provides
excellent resolution to not only be used in high-throughput applications and dynamics, but
also has the potential for reliable quantified measurements – something that is not feasible
with traditional AFM on varied sample surfaces. Such developments allow HS-AFM
technology to begin to challenge the stereotype of AFM being too “slow” for industry and
be used for materials science quality control (QC) inspection applications.
This work primarily explores the current and future potential of HS-AFM in the area of
QC. Benchmarking of the HS-AFM was conducted to evaluate its strengths and limitations,
before demonstrating its reliability in the quantification of fibre roughness as a challenging
sample set for AFM technology to measure well. Two methods of roughness quantification
are compared to address the challenges posed, with impressive consistency shown between
subsequent datasets.
Further work sought to automate the filtering of anomalous frames in large HS-AFM
datasets using machine learning, as a route for development to increase the appeal of
HS-AFM to industry. Familiarity of the HS-AFM allowed for the critical assessment of a
newly released HS-AFM to highlight how the advancements make it better suited for QC.
The impact of this work will increase the adoption of HS-AFM technology in industry,
helping fill a niche of instruments available for QC, so that new protocols can be established
to maintain and improve the quality of many materials and nanoscale devices in use today.