Machine learning inspired nanowire classification method based on nanowire array scanning electron microscope images

Brugnolotto, E., Aleksandrov, P. , Sousa, M. and Georgiev, V. (2024) Machine learning inspired nanowire classification method based on nanowire array scanning electron microscope images. Open Research Europe, 4, 43. (doi: 10.12688/openreseurope.16696.1)

Abstract

Background: This article introduces an innovative classification methodology to identify nanowires within scanning electron microscope images. Methods: Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. Results: The method’s capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model’s proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Conclusions: Such a high level of performance and accuracy of ML methods demonstrate the viability of our technique not only for academic but also for practical commercial implementation and usage.

• Item Type: Articles
• Additional Information: Version 1; peer review: awaiting peer review. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No [860095].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Georgiev, Professor Vihar and Aleksandrov, Mr Preslav and Brugnolotto, Mr Enrico
• Authors: Brugnolotto, E., Aleksandrov, P., Sousa, M., and Georgiev, V.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Open Research Europe
• Publisher: F1000Research
• ISSN: 2732-5121
• ISSN (Online): 2732-5121
• Copyright Holders: Copyright © 2024 Brugnolotto E et al.
• First Published: First published in Open Research Europe 4:43
• Publisher Policy: Reproduced under a Creative Commons license

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
306142		Defect Simulation and Material Growth of III-V Nanostructures- European Industrial Doctorate Program	Vihar Georgiev	European Commission (EC)	860095	ENG - Electronics & Nanoscale Engineering