Document Type
Article
Publication Date
3-11-2021
Publication Title
Scientific Reports
First page number:
1
Last page number:
8
Abstract
Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
Keywords
Image segmentation; Optical identification; 2D materials; Machine learning
Disciplines
Engineering | Materials Science and Engineering | Semiconductor and Optical Materials
File Format
File Size
146 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Island, J.,
Sterbentz, R. M.,
Haley, K. L.
(2021).
Universal Image Segmentation for Optical Identification of 2D Materials.
Scientific Reports
1-8.
http://dx.doi.org/10.1038/s41598-021-85159-9