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Nanowire Detection in AFM Images Using Deep Learning

Published online by Cambridge University Press:  17 November 2020

Huitian Bai Open the ORCID record for Huitian Bai [Opens in a new window]  and
Sen Wu Open the ORCID record for Sen Wu [Opens in a new window]
Huitian Bai
Affiliation:
State Key Lab of Precision Measurement Technology and Instruments, Tianjin University, Tianjin300072, P.R. China
Sen Wu*
Affiliation:
State Key Lab of Precision Measurement Technology and Instruments, Tianjin University, Tianjin300072, P.R. China Nanchang Institute for Microtechnology of Tianjin University, Tianjin300072, P.R. China
*
*Author for correspondence: Sen Wu, E-mail: senwu@tju.edu.cn
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Abstract

Atomic force microscope (AFM) based nanomanipulation has been proved to be a possible way to assemble various nanoparticles into complex patterns and devices. To achieve an efficient and full-automatic nanomanipulation, the nanoparticles as well as other features on the substrate must be quickly identified by the computer. This work focuses on an autodetection method for flexible nanowires based on deep learning. The You Only Look Once (YOLO) network is applied to find all movable nanowires in the AFM images. A series of morphology transformation algorithms, including an adaptive threshold edge detection, are applied to refine the skeletons of the nanowires. The bidirectional long short-term memory model with conditional random field layer (BI-LSTM-CRF) is proposed to precisely determine the posture and position of the detected nanowires. Benefiting from these algorithms, our detecting program is able to automatically detect the nanowires of different morphology with nanometer resolution and with over 80% reliability in the testing set. The detecting results are less affected by the image quality, which demonstrates good robustness of this algorithm.

Keywords

AFMBI-LSTM-CRFdeep learningnanowire detectionYOLO
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 27 , Issue 1 , February 2021 , pp. 54 - 64
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of the Microscopy Society of America

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