Evidence map›Paper›PMID 36144115›Full record

ArticleMicromachines2022

High-Efficiency Copper Removal by Nitrogen Plasma-Assisted Picosecond Laser Processing.

Yunfan Li, Xuanqi Guo, Shuai Wang, Shizhuo Zhang, Yilin Zhao, Dingyi Guo, Chen Zhang, Sheng Liu, Gary J Cheng, Feng Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yunfan LiSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Xuanqi GuoSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Shuai WangInstitute of Technological Sciences, Wuhan University, Wuhan 430072, China.
Shizhuo ZhangInstitute of Technological Sciences, Wuhan University, Wuhan 430072, China.
Yilin ZhaoSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Dingyi GuoSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Chen ZhangInstitute of Technological Sciences, Wuhan University, Wuhan 430072, China.
Sheng LiuSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Gary J ChengSchool of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA.
Feng LiuSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.ORCID 0000-0002-3301-7613

Funding

National Key Research and Development Program of China 2018YFB1107700Natural Science Foundation of Hubei Province of China 2020CFA032
6 · The paper itself

Abstract

Copper (Cu) removal efficiency is a key parameter in the processing of Cu-based electronic devices. Herein, a nitrogen plasma-assisted picosecond (ps) laser process for Cu removal is presented. Based on the cleaning and activation effect of nitrogen plasma on the surface of Cu film in ps-laser ablation, the removal efficiency can be significantly improved. Theoretically, the interaction mechanism between Cu and the ps-laser under the action of the plasma flow field is investigated by the dual temperature model (TTM) and finite element analysis (FEA). Meanwhile, the experimental results show that the angle of the plasma flow significantly affects the laser ablation of Cu. Small-angle plasma helps to improve the ps-laser processing precision of Cu, while large-angle plasma can effectively improve the ps-laser processing efficiency of Cu. Under the laser fluence of 2.69 J/cm

Indexed as

Cu removalhigh-efficiencynitrogen plasmaps-laser

Identifiers

PMID36144115
PMCPMC9504670

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.