Fully Automated Identification of Two-Dimensional Material Samples
Eliška Greplová, Carolin Gold, Benedikt Kratochwil, Tim Davatz, Riccardo Pisoni, Annika Kurzmann, Peter Rickhaus, Mark H. Fischer, Thomas Ihn, Sebastian D. Huber · Zurich Open Repository and Archive (University of Zurich) · 2020
Thin nanomaterials are key constituents of modern quantum technologies and materials research. The identification of specimens of these materials with the properties required for the development of state-of-the-art quantum devices is usually a complex and tedious human task. In this work, we provide a neural-network-driven solution that allows for accurate and efficient scanning, data processing, and sample identification of experimentally relevant two-dimensional materials. We show how to approach the classification of imperfect and imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification.