Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials

Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS₂, and WTe₂). The inference algorithm is run on a 1024 × 1024 px optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize a large number of 2D materials simply by exfoliating and running the automated searching process. To facilitate research, we make the training codes, dataset, and model weights publicly available.

Satoru Masubuchi, Eisuke Watanabe, Yuta Seo, Shota Okazaki, Takao Sasagawa, Kenji Watanabe, Takashi Taniguchi, Tomoki Machida