Multi-view neural 3D reconstruction of micro- and nanostructures with atomic force microscopy

Atomic Force Microscopy (AFM) is a widely employed tool for micro- and nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro- and 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, named MVN-AFM, which accurately reconstructs surface models of intricate micro- and nanostructures. Unlike previous 3D-AFM approaches, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we apply the neural implicit surface reconstruction technique in nanotechnology and achieve improved results. Additional extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro- and nanostructures, including complex geometrical microstructures printed via two-photon lithography and nanoparticles such as poly(methyl methacrylate) (PMMA) nanospheres and zeolitic imidazolate framework-67 (ZIF-67) nanocrystals. This work presents a cost-effective tool for micro- and nanoscale 3D analysis. Shuo Chen and colleagues present a cost-effective neural network-based method to deal with tip-sample convolution artifacts in atomic force microscopy. Their method merges multiview atomic force microscopy images into precise 3D models of complex micro- and nanostructures.