Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning

Understand

In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems.

• Instance segmentation can be considered as a potential approach to solve this problem.
• However, similar to other deep learning methods, the instance segmentation requires a large scale training dataset and high quality annotation in order to achieve a considerable performance.
• In practice, preparing the training dataset is a challenge since annotators have to deal with a large image, e.g 2K resolution, and extremely dense objects in this problem.