Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials
Abstract
Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural‐network‐based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real‐time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial‐intelligence‐based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 09, 2020
- Source ID
- 10.1002/adma.202000953
Entities
People
- Bingnan Han
- Cao Yuan
- Dahlia R Klein
- Daniel Rodan‐legrain
- David Macneill
- Efrén Navarro‐moratalla
- Haozhe Wang
- Hikari Kitadai
- Jihao Yin
- Jing Kong
- Joel I.‐jan Wang
- Kenji Yasuda
- Lin Zhou
- Nannan Mao
- Pablo Jarillo‐herrero
- Qiong Ma
- Sanfeng Wu
- Tomás Palacios
- Valla Fatemi
- Wenyue Li
- Xi Ling
- Xirui Wang
- Yafang Yang
- Ya‐Qing Bie
- Yuxuan Lin
Organizations
- Air Force Office of Scientific Research
- Army Research Office
- Beihang University
- Boston University
- China Scholarship Council
- Gordon and Betty Moore Foundation
- Massachusetts Institute of Technology
- National Natural Science Foundation of China
- National Science Foundation
- Office of Basic Energy Sciences
- Office of Science
- United States Department of Energy
- University of Valencia