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

https://mdr.nims.go.jp/datasets/5d818258-3ecf-46ee-855a-44db72ab22da

## File

- [Uslu_2024_Mach._Learn.-_Sci._Technol._5_015027.pdf](https://mdr.nims.go.jp/filesets/f41b74e7-cc68-4ce3-843f-5b6e00631c3a/download) ([Detail](https://mdr.nims.go.jp/filesets/f41b74e7-cc68-4ce3-843f-5b6e00631c3a.md))

## Id

5d818258-3ecf-46ee-855a-44db72ab22da

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-02-14T04:11:49.079235Z

## Updated at

2025-02-14T07:30:31.502707Z

## Published at

2025-02-14T07:30:31.627908Z

## Doi



## First published url

https://doi.org/10.1088/2632-2153/ad2287

## Date published

2024-03-01

## Recorded date published

2024-3-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: An open-source robust machine learning platform for real-time detection and
    classification of 2D material flakes
  title_type: original
  lang: en

## Description

- description: The most widely used method for obtaining high-quality two-dimensional
    materials is through mechanical exfoliation of bulk crystals. Identifying suitable
    flakes from a random distribution of crystal thicknesses and sizes on a substrate
    is typically done manually, which is time-consuming and tedious and suitable flakes
    can easily be overlooked. 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
    and fast as well as versatile in terms of integrating new materials, making it
    suitable for everyday laboratory work. The implementation allows a fully automized
    scanning and analysis of wafers with an average inference time of 100 ms for 2.3
    MPixel images. The developed detection algorithm is based on a combination of
    the flakes’ optical contrast towards 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) of different materials between
    66% and 92%. 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, WSe2, CrI3, 1T-TaS2 and hBN. Additionally, we discuss the stability
    of the approach to variations in the oxide thickness of the wafers used for exfoliation.
    Our platform has been tested over a period of two years, during which over 106
    images of multiple different materials were acquired by over 30 individual researchers.
  description_type: abstract
  lang: und

## Creator

- name: Jan-Lucas Uslu
  role: author
- name: Taoufiq Ouaj
  role: author
- name: David Tebbe
  role: author
- name: Alexey Nekrasov
  role: author
- name: Jo Henri Bertram
  role: author
- name: Marc Schütte
  role: author
- name: Kenji Watanabe
  role: author
  orcid: https://orcid.org/0000-0003-3701-8119
  organization: National Institute for Materials Science
- name: Takashi Taniguchi
  role: author
  orcid: https://orcid.org/0000-0002-1467-3105
  organization: National Institute for Materials Science
- name: Bernd Beschoten
  role: author
- name: Lutz Waldecker
  role: author
- name: Christoph Stampfer
  role: author

## Contact agent



## Publisher

organization: IOP Publishing

## Managing organization



## Keyword

- subject: Automated scanning
  schema: not_defined
- subject: 2D materials
  schema: not_defined
- subject: detection algorithm
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Machine Learning: Science and Technology'
  issn: '26322153'
  volume: '5'
  issue: '1'
  article_number: '015027'

## Conference



## Related item



## Funding

- identifier: '820254'
  funder_name: European Research Council
- identifier: 20H00354
  funder_name: Japan Society for the Promotion of Science
- identifier: '437214324'
  funder_name: Deutsche Forschungsgemeinschaft
- identifier: '881603'
  funder_name: Graphene Flagship

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: f41b74e7-cc68-4ce3-843f-5b6e00631c3a
  filename: Uslu_2024_Mach._Learn.-_Sci._Technol._5_015027.pdf
  content_type: application/pdf
  size: 5040148
  md5: 627970a6c2740ddfea7a706bddcc02c3

## Thumbnail

fileset_id: f41b74e7-cc68-4ce3-843f-5b6e00631c3a
filename: Uslu_2024_Mach._Learn.-_Sci._Technol._5_015027.pdf