Precise Surface Profiling at the Nanoscale Enabled by Deep Learning

Surface topography, or height profile,
is a critical property for
various micro- and nanostructured materials and devices, as well as
biological systems. At the nanoscale, atomic force microscopy (AFM)
is the tool of choice for surface profiling due to its capability
to noninvasively map the topography of almost all types of samples.
However, this method suffers from one drawback: the convolution of
the nanoprobe’s shape in the height profile of the samples,
which is especially severe for sharp protrusion features. Here, we
report a deep learning (DL) approach to overcome this limit. Adopting
an image-to-image translation methodology, we use data sets of tip-convoluted
and deconvoluted image pairs to train an encoder–decoder based
deep convolutional neural network. The trained network successfully
removes the tip convolution from AFM topographic images of various
nanocorrugated surfaces and recovers the true, precise 3D height profiles
of these samples.