Abstract:Advances in scanning probe microscopy (SPM) methods such
as time-resolved
electrostatic force microscopy (trEFM) now permit the mapping of fast
local dynamic processes with high resolution in both space and time,
but such methods can be time-consuming to analyze and calibrate. Here,
we design and train a regression neural network (NN) that accelerates
and simplifies the extraction of local dynamics from SPM data directly
in a cantilever-independent manner, allowing the network to process
data taken with differ… Show more
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