• Corpus ID: 207760681

Fully automated identification of 2D material samples.

  title={Fully automated identification of 2D material samples.},
  author={Eliska Greplova and Carolin Gold and Benedikt Kratochwil and Tim Davatz and Riccardo Pisoni and Annika Kurzmann and Peter Rickhaus and Mark H. Fischer and Thomas Ihn and Sebastian D. Huber},
  journal={arXiv: Mesoscale and Nanoscale Physics},
Thin nanomaterials are key constituents of modern quantum technologies and materials research. Identifying specimens of these materials with properties required for the development of state of the art quantum devices is usually a complex and lengthy 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… 

Figures and Tables from this paper

Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks

An algorithm is introduced that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state and consistently arrives at the desired state or its immediate neighborhood.

Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials

A deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional materials and integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials.



Deep-learning-based quality filtering of mechanically exfoliated 2D crystals

Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy, and biology owing to their low dimensionality and dramatic

Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in

Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning

Machine-learning techniques enable recognition of a wide range of images, complementing human intelligence. Since the advent of exfoliated graphene on SiO2/Si substrates, identification of graphene

Rapid and reliable thickness identification of two-dimensional nanosheets using optical microscopy.

A universal optical method has been developed for simple, rapid, and reliable identification of single- to quindecuple-layer (1L-15L) 2D nanosheets on Si substrates coated with 90 or 300 nm SiO2.

Making graphene visible

Microfabrication of graphene devices used in many experimental studies currently relies on the fact that graphene crystallites can be visualized using optical microscopy if prepared on top of Si

Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

This work reviews the historical development of Transition metal dichalcogenides, methods for preparing atomically thin layers, their electronic and optical properties, and prospects for future advances in electronics and optoelectronics.

Hunting for monolayer boron nitride: optical and Raman signatures.

We describe the identification of single- and few- layer boron nitride. Its optical contrast is much smaller than that of graphene but even monolayers are discernable by optimizing viewing

Emerging device applications for semiconducting two-dimensional transition metal dichalcogenides.

By critically assessing and comparing the performance of these devices with competing technologies, the merits and shortcomings of this emerging class of electronic materials are identified, thereby providing a roadmap for future development.

Boron nitride substrates for high-quality graphene electronics.

Graphene devices on h-BN substrates have mobilities and carrier inhomogeneities that are almost an order of magnitude better than devices on SiO(2).