An open-source robust machine learning platform for real-time detection and classification of 2D material flakes

TL;DR

This paper introduces an open-source, automated platform for real-time detection and classification of 2D material flakes, significantly speeding up and improving the accuracy of identifying suitable flakes in laboratory settings.

Contribution

The authors developed a versatile, fast, and reliable platform with a detection algorithm capable of training with minimal data, applicable to various 2D materials, and made the source code openly available.

Findings

Average inference time of 100 ms per image

Detection recall (AR50) between 67% and 89% for various materials

Effective training with as few as five flakes per material

Abstract

The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of two-dimensional materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes' optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe$_2$, MoSe$_2$, CrI$_3$, 1T-TaS$_2$ and hexagonal BN. Our platform has been tested over a two-year period, during which more than $10^6$ images of multiple different materials were acquired by over 30 individual researchers.