Deep-learning-based nanowire detection in AFM images for automated nanomanipulation

Abstract

Atomic force microscope (AFM)-based nanomanipulation has been proved to be a possible method for assembling various nanoparticles into complex patterns and devices. To achieve efficient and fully automated nanomanipulation, nanoparticles on the substrate must be identified precisely and automatically. This work focuses on an autodetection method for flexible nanowires using a deep learning technique. An instance segmentation network based on You Only Look Once version 3 (YOLOv3) and a fully convolutional network (FCN) is applied to segment all movable nanowires in AFM images. Combined with follow-up image morphology and fitting algorithms, this enables detection of postures and positions of nanowires at a high abstraction level. Benefitting from these algorithms, our program is able to automatically detect nanowires of different morphologies with nanometer resolution and has over 90% reliability in the testing dataset. The detection results are less affected by image complexity than the results of existing methods and demonstrate the good robustness of this algorithm.