Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy

• URL: http://arxiv.org/abs/2401.11541v1
• Date: Sun, 21 Jan 2024 16:46:04 GMT
• Title: Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
• Authors: Shuo Chen, Mao Peng, Yijin Li, Bing-Feng Ju, Hujun Bao, Yuan-Liu Chen, Guofeng Zhang
• Abstract summary: We propose a multi-view neural-network-based framework with Atomic Force Microscopy (MVN-AFM) MVN-AFM uses an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro-/nanostructures.
• Score: 34.22380991792944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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