Abstract
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.
基金项目
国家重点研发计划(2022YFB2803900)
国家自然科学基金(61974075)
国家自然科学基金(61704121)
天津市自然科学基金(22JCZDJC00460)
天津市自然科学基金(19JCQNJC00700)
天津市教委项目(2019KJ028)
中央高校基本科研业务费专项(22JCZDJC00460)
Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin and the Engineering Research Center of Thin Fi()
维普
万方数据