  Atomic Force Microscopy (AFM) is a widely employed tool for micro-/nanoscale
topographic imaging. However, conventional AFM scanning struggles to
reconstruct complex 3D micro-/nanostructures precisely due to limitations such
as incomplete sample topography capturing and tip-sample convolution artifacts.
Here, we propose a multi-view neural-network-based framework with AFM
(MVN-AFM), which accurately reconstructs surface models of intricate
micro-/nanostructures. Unlike previous works, MVN-AFM does not depend on any
specially shaped probes or costly modifications to the AFM system. To achieve
this, MVN-AFM uniquely employs an iterative method to align multi-view data and
eliminate AFM artifacts simultaneously. Furthermore, we pioneer the application
of neural implicit surface reconstruction in nanotechnology and achieve
markedly improved results. Extensive experiments show that MVN-AFM effectively
eliminates artifacts present in raw AFM images and reconstructs various
micro-/nanostructures including complex geometrical microstructures printed via
Two-photon Lithography and nanoparticles such as PMMA nanospheres and ZIF-67
nanocrystals. This work presents a cost-effective tool for micro-/nanoscale 3D
analysis.
