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.
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)
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).
