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Machine Learning Inspired Nanowire Classification Method based on Nanowire Array Scanning Electron Microscope Images
Enrico Brugnolotto1,2, Preslav Aleksandrov1, Marilyne Sousa2
1James Watt School of Engineering, University of Glasgow, Glasgow, Scotland, UK.
Open Research Europe
|July 3, 2024
Summary
This study presents a new method using machine learning to accurately identify nanowires in scanning electron microscope images. The technique achieves high precision and recall, showing potential for both research and industry applications.
Area of Science:
- Materials Science
- Nanotechnology
- Computer Science
Background:
- Scanning electron microscopy (SEM) is crucial for visualizing nanoscale structures.
- Accurate identification of nanowires in SEM images is essential for material characterization.
Purpose of the Study:
- To develop an innovative classification methodology for identifying nanowires in SEM images.
- To demonstrate the effectiveness of a machine learning (ML)-based approach for nanowire categorization.
Main Methods:
- Utilized advanced image manipulation techniques.
- Employed machine learning-based recognition algorithms for classification.
- Trained models on SEM images of III-V nanowire arrays grown via metal organic chemical vapor deposition.
Main Results:
- Achieved an average F1 score of 0.91, indicating high precision and recall.
- Demonstrated proficiency in isolating and distinguishing individual nanowires within arrays.
- Successfully detected parasitic crystals alongside nanowires.
Conclusions:
- The ML-based method offers high accuracy and performance for nanowire identification.
- The technique is viable for both academic research and practical commercial applications.
- This approach enhances the analysis of SEM images in nanotechnology.
