  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 x 1024 px2 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 unlimited amounts
of 2D materials simply by exfoliating and running the automated searching
process.
