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

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.