Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning

Autor:	Smalley, Darian, Lough, Stephanie D., Holtzman, Luke, Xu, Kaikui, Holbrook, Madisen, Rosenberger, Matthew R., Hone, J. C., Barmak, Katayun, Ishigami, Masahiro
Rok vydání:	2023
Předmět:	Condensed Matter - Materials Science Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an average F1 score of 0.66 and demonstrated ensemble generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput. Comment: 9 pages, 4 figures, submitted to MRS Advances as a conference preceding for the 2023 MRS Fall Meeting & Exhibit
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.05160 Zobrazit plný text záznamu View this record from Arxiv