Automated Tabletop Exfoliation and Identification of Monolayer Graphene Flakes

Abstract

Over the past two decades, graphene has been intensively studied because of its remarkable mechanical, optical, and electronic properties. Initial studies were enabled by manual ``Scotch Tape'' exfoliation; nearly two decades later, this method is still widely used to obtain chemically-pristine flakes of graphene and other 2D van der Waals materials. Unfortunately, the yield of large, pristine flakes with uniform thickness is inconsistent. Thus, significant time and effort are required to exfoliate and locate flakes suitable for fabricating multilayer van der Waals heterostructures. Here, we describe a relatively affordable tabletop device (the ``eXfoliator'') that can reproducibly control key parameters and largely automate the exfoliation process. In a typical exfoliation run, the eXfoliator produces 3 or more large ($\ge 400\mu$m${}^2$) high-quality graphene monolayer flakes, allowing new users to produce such flakes at a rate comparable to manual exfoliation by an experienced user. We use an automated mapping system and a computer vision algorithm to locate candidate flakes. Our results provide a starting point for future research efforts to more precisely identify which parameters matter for the success of exfoliation, and to optimize them.

11 pages, 10 figures, 12 tables

References in corpus (10)

• Electric Field Effect in Atomically Thin Carbon Films
• Universal Dynamic Conductivity and Quantized Visible Opacity of Suspended Graphene
• Thermal properties of graphene: Fundamentals and applications
• Fractional Chern insulators in magic-angle twisted bilayer graphene
• Superconductivity in rhombohedral trilayer graphene
• Isospin magnetism and spin-triplet superconductivity in Bernal bilayer graphene
• Correlated Insulator and Chern Insulators in Pentalayer Rhombohedral Stacked Graphene
• Orbital Multiferroicity in Pentalayer Rhombohedral Graphene
• Tunable ferromagnetism at non-integer filling of a moiré superlattice
• An open-source robust machine learning platform for real-time detection and classification of 2D material flakes