Deep-Learning-Based Image Segmentation Integrated with Optical Microscopy for Automatically Searching for Two-Dimensional Materials

TL;DR

This paper presents a deep-learning-based image segmentation algorithm integrated with optical microscopy for real-time, automated detection and cataloging of two-dimensional materials, improving robustness and efficiency over traditional methods.

Contribution

The authors developed a Mask-RCNN based segmentation algorithm that enables real-time, robust detection of 2D materials in optical microscopy images, integrated into an autonomous robotic system.

Findings

Real-time detection within 200 ms per image

Robustness against illumination and color variations

Automated searching and cataloging of 2D materials

Abstract

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.