Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network

被引:12
|
作者
Mahjoubi, Soroush [1 ]
Ye, Fan [2 ]
Bao, Yi [1 ]
Meng, Weina [1 ]
Zhang, Xian [3 ]
机构
[1] Stevens Inst Technol, Dept Civil Environm & Ocean Engn, Hoboken, NJ 07030 USA
[2] Univ Massachusetts Amherst, Dept Elect & Comp Engn, Amherst, MA 01003 USA
[3] Stevens Inst Technol, Dept Mech Engn, Hoboken, NJ 07030 USA
基金
美国国家科学基金会;
关键词
Deep convolutional neural network; Machine learning; Nanomaterials; Optimized adaptive gamma correction; Semantic segmentation; Two-dimensional (2D) material; MOS2;
D O I
10.1016/j.engappai.2022.105743
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Identification of exfoliated graphene flakes and classification of the thickness are important in the nanomanufacturing of advanced materials and devices. This paper presents a deep learning method to automatically identify and classify exfoliated graphene flakes on Si/SiO2 substrates from optical microscope images. The presented framework uses a hierarchical deep convolutional neural network that is capable of learning new images while preserving the knowledge from previous images. The deep learning model was trained and used to classify exfoliated graphene flakes into monolayer, bi-layer, tri-layer, four-to-six-layer, seven-to-ten layer, and bulk categories. Compared with existing machine learning methods, the presented method showed high accuracy and efficiency as well as robustness to the background and resolution of images. The results indicated that the pixel-wise accuracy of the trained deep learning model was 99% in identifying and classifying exfoliated graphene flakes. This research will facilitate scaled-up manufacturing and characterization of graphene for advanced materials and devices.
引用
收藏
页数:12
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