# Predicting the surface roughness of an electrodeposited copper film using a machine learning technique

https://mdr.nims.go.jp/datasets/5bc7ba84-83f9-43e6-9de5-e6f1aa5bc0e4

## File

- [Predicting the surface roughness of an electrodeposited copper film using a machine learning technique.pdf](https://mdr.nims.go.jp/filesets/8e0b3c7e-e6a8-4a30-80e8-e74e52bf0ae2/download) ([Detail](https://mdr.nims.go.jp/filesets/8e0b3c7e-e6a8-4a30-80e8-e74e52bf0ae2.md))

## Id

5bc7ba84-83f9-43e6-9de5-e6f1aa5bc0e4

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-29T19:23:56.823070Z

## Updated at

2024-10-31T07:30:15.122586Z

## Published at

2024-10-31T07:30:16.339608Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2024.2416889

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Predicting the surface roughness of an electrodeposited copper film using
    a machine learning technique
  title_type: original
  lang: en

## Description

- description: Electrodeposition-based metal coating techniques are used to manufacture
    various industrial products and rely on the quantitative control of the physical
    properties of the coating layers, such as electrical conductivity, surface roughness,
    and hardness. To clarify the experimental conditions required to realize the desired
    physical properties of metal coating layers and shed light on the complex mechanism
    of the involved reactions, we prepared a custom-built experimental dataset (60
    conditions) on the surface roughness of electrodeposited thin copper films and
    submitted it to an open-access data repository. Data-driven analysis revealed
    that surface roughness is strongly affected by the deposition temperature, current,
    and interelectrode distance.
  description_type: abstract
  lang: eng

## Creator

- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Algorithm Team
  ror: https://ror.org/026v1ze26
- name: Ryuichi Inaba
  role: author
  organization: MITSUBISHI MATERIALS CORPORATION
- name: Mami Watanabe
  role: author
  organization: MITSUBISHI MATERIALS CORPORATION
- name: Yutaro Mori
  role: author
  organization: MITSUBISHI MATERIALS CORPORATION
- name: Makoto Urushihara
  role: author
  organization: MITSUBISHI MATERIALS CORPORATION
- name: Kenji Yamaguchi
  role: author
  organization: MITSUBISHI MATERIALS CORPORATION
- name: Shoichi Matsuda
  role: author
  orcid: https://orcid.org/0000-0002-0640-3404
  organization: National Institute for Materials Science
  department: Research Center for Energy and Environmental Materials (GREEN)/Battery
    and Cell Materials Field/Automated Electrochemical Experiments Team
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: electrodeposited copper film
  schema: not_defined
- subject: surface roughness
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '4'
  issue: '1'
  article_number: '2416889'

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## Measurement method



## Specimen



## Chemical composition



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## Fileset

- id: 8e0b3c7e-e6a8-4a30-80e8-e74e52bf0ae2
  filename: Predicting the surface roughness of an electrodeposited copper film using
    a machine learning technique.pdf
  content_type: application/pdf
  size: 4925741
  md5: 9fb486d9f87551085313544e98f41b28

## Thumbnail

fileset_id: 8e0b3c7e-e6a8-4a30-80e8-e74e52bf0ae2
filename: Predicting the surface roughness of an electrodeposited copper film using
  a machine learning technique.pdf