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

Huitian Bai,Sen Wu

doi:10.1063/10.0003218

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.

Highlights

The assembly of nanoparticles, including a variety of lowdimensional materials, represents one of the most important areas of current nanotechnology
We train an instance segmentation network based on You Only Look Once version 3 (YOLOv3)23–25 and a fully convolutional network (FCN)26 to segment movable nanowires in atomic force microscopy (AFM) images at a high level of abstraction
The present nanowire detection algorithm is applied to the AFM images in Figs. 6(a)-6(f), giving the results shown in Figs. 6(a′)6(f′), including the bounding boxes and the polygonal lines representing nanowires

Summary

Introduction

The assembly of nanoparticles, including a variety of lowdimensional materials, represents one of the most important areas of current nanotechnology. Differing from optical microscopes, an AFM captures an image by sampling the atomic forces between a probe and the specimen in fixed steps along x and y axes on a given region With this mechanical method, it is possible to manipulate nanoparticles using the forces produced by pushing them with AFM probes. If this path passes through a nanoparticle, the nanoparticle can be pushed by the probe to the target position This process of manipulation is time-consuming because imaging and manipulation using AFM cannot be executed simultaneously. An image must be captured after each path of manipulation in order to check whether the nanoparticle has reached its target position For many reasons, this process will unavoidably repeat many times, and the efficiency of nanomanipulation is very low. Automated nanomanipulation technology aims to enhance the efficiency of manipulating objects with AFM by decreasing the frequency of imaging required between rounds of manipulation

Methods

Results

Conclusion