Document Type
Article
Publication Date
9-2024
Abstract
Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. Second, we introduce an attention-based loss function to mitigate the negative impact of these unannotated flakes on the DNNs. The experimental results demonstrate that our method outperforms previous approaches.
Citation
Nguyen, X., Bisht, A., Thompson, B., Churchill, H. O., Luu, K., & Khan, S. U. (2024). Two-dimensional Quantum Material Identification Via Self-attention and Soft-labeling in Deep Learning. IEEE Access, 12, 139683-139691. https://doi.org/10.1109/ACCESS.2024.3465221
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Keywords
Microscopy; Optical imaging; Optical microscopy; Substrates; Quantum materials; Image color analysis; Annotations; Deep learning; Computer vision; Quantum material; 2D flake detection; deep learning; computer vision; identification
