Automated and fast recognition of exfoliated two-dimensional materials .

Two-dimensional (2D) materials are a class of nanomaterials that consist of a single- or few-layers of atoms and possess exceptional physical and chemical properties. Such unique properties of 2D materials made them a focal point of research on novel nano-electronic devices. One of the easiest and widely used laboratory method of making 2D materials is the Mechanical Exfoliation also known as the Scotch Tape method, where flakes of various size and thickness are randomly split from bulk materials and transferred to a chosen substrate using adhesive tape. Then flakes need to be identified on the substrate and that process is usually done manually using optical microscopy as a starting tool and other lower-throughput methods for more reliable final identification. Manual search and identification of flakes is a time consuming and involved process that requires expertise to do it efficiently. There have been efforts to automate flake search and characterization using machine learning (ML). However, ML has some drawbacks: high volume of training data needed, variable samples/substrates, high requirement for computing power or time. Proposed solution to the efficient recognition and sorting of 2D material supported by the automation is to use a general non-ML algorithm that relies on some user input and general visual properties of 2D material flakes to identify and possibly characterize flakes with minimum computing power/time required.