Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
This provides a cost-effective tool for micro-/nanoscale 3D analysis in nanotechnology, though it is incremental as it builds on existing neural implicit surface reconstruction methods by applying them to a new domain.
The paper tackles the problem of accurately reconstructing 3D micro-/nanostructures from Atomic Force Microscopy (AFM) data, which suffers from incomplete topography and artifacts, by proposing a multi-view neural-network framework (MVN-AFM) that eliminates artifacts and reconstructs complex structures like microstructures and nanoparticles.
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.