Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation  

作　　者：程晓昱 解晨雪 刘宇伦 白瑞雪 肖南海 任琰博 张喜林 马惠 蒋崇云 Xiaoyu Cheng;Chenxue Xie;Yulun Liu;Ruixue Bai;Nanhai Xiao;Yanbo Ren;Xilin Zhang;Hui Ma;Chongyun Jiang(College of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,China;School of Physical Science and Technology,Tiangong University,Tianjin 300387,China)

机构地区：[1]College of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,China [2]School of Physical Science and Technology,Tiangong University,Tianjin 300387,China

出　　处：《Chinese Physics B》2024年第3期112-117,共6页中国物理B（英文版）

基　　金：Project supported by the National Key Research and Development Program of China(Grant No.2022YFB2803900);the National Natural Science Foundation of China(Grant Nos.61974075 and 61704121);the Natural Science Foundation of Tianjin Municipality(Grant Nos.22JCZDJC00460 and 19JCQNJC00700);Tianjin Municipal Education Commission(Grant No.2019KJ028);Fundamental Research Funds for the Central Universities(Grant No.22JCZDJC00460).

摘　　要：Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

关 键 词：two-dimensional materials deep learning data augmentation generating adversarial networks 

分 类 号：TB34[一般工业技术—材料科学与工程] TP391.41[自动化与计算机技术—计算机应用技术]