Deep Learning Analysis of Polaritonic Wave Images

Autor:	Dmitri Basov, Zhicai Wang, Sara Shabani, Zhiyuan Sun, Xinzhong Chen, Bjarke Sørensen Jessen, Cory Dean, James Hone, Ziheng Yao, Alexander McLeod, Daniel J. Rizzo, Andrew J. Millis, Suheng Xu, Mengkun Liu, Abhay Pasupathy
Rok vydání:	2021
Předmět:	business.industry Computer science Deep learning General Engineering General Physics and Astronomy General Materials Science Pattern recognition Artificial intelligence business Convolutional neural network Imaging data
Zdroj:	ACS Nano. 15:18182-18191
ISSN:	1936-086X 1936-0851
Popis:	Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00dc68c7ba6582ea40b9d0c5e94a9bc0 https://doi.org/10.1021/acsnano.1c07011 Zobrazit plný text záznamu