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

被引：0

作者：

Tamura, Ryo ^{[1
,2
]}

Inaba, Ryuichi ^{[3
]}

Watanabe, Mami ^{[3
]}

Mori, Yutaro ^{[3
]}

Urushihara, Makoto ^{[3
]}

Yamaguchi, Kenji ^{[3
]}

Matsuda, Shoichi ^{[4
]}

机构：

[1] Natl Inst Mat Sci, Ctr Basic Res Mat, 1-1 Namiki, Tsukuba, Ibaraki 3050044, Japan

[2] Univ Tokyo, Grad Sch Frontier Sci, Kashiwa, Chiba, Japan

[3] Mitsubishi Mat Corp, Innovat Ctr, 1002-14 Mukohyama, Naka 3110102, Japan

[4] Natl Inst Mat Sci, Res Ctr Energy & Environm Mat GREEN, 1-1 Namiki, Tsukuba, Ibaraki 3050044, Japan

来源：

SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS | 2024年 / 4卷 / 01期

关键词：

Electrodeposition; machine learning; copper film; electrochemistry; surface roughness;

D O I：

10.1080/27660400.2024.2416889

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：8

共 25 条

[1] Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science
Agrawal, Ankit
Choudhary, Alok
[J]. APL MATERIALS, 2016, 4 (05):
[2] Brist G., 2005, ECWC 10 Conference at IPC Printed Circuits Expo, S19-2-1
[3] Review-Management of Copper Damascene Plating
Carpio, R.
Jaworski, A.
[J]. JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2018, 166 (01) : D3072 - D3096
[4] Chen ZH, 2010, 2010 11TH INTERNATIONAL CONFERENCE ON ELECTRONIC PACKAGING TECHNOLOGY & HIGH DENSITY PACKAGING (ICEPT-HDP), P433, DOI 10.1109/ICEPT.2010.5583828
[5] Machine Learning Approach to the Prediction of Surface Roughness using Statistical Features of Vibration Signal Acquired in Turning
Elangovan, M.
Sakthivel, N. R.
Saravanamurugan, S.
Nair, Binoy B.
Sugumaran, V.
[J]. BIG DATA, CLOUD AND COMPUTING CHALLENGES, 2015, 50 : 282 - 288
[6] Mechanism of copper deposition in a sulphate bath containing chlorides
Gabrielli, C
Moçotéguy, P
Perrot, H
Wiart, R
[J]. JOURNAL OF ELECTROANALYTICAL CHEMISTRY, 2004, 572 (02) : 367 - 375
[7] Electroplating for Decorative Applications: Recent Trends in Research and Development
Giurlani, Walter
Zangari, Giovanni
Gambinossi, Filippo
Passaponti, Maurizio
Salvietti, Emanuele
Di Benedetto, Francesco
Caporali, Stefano
Innocenti, Massimo
[J]. COATINGS, 2018, 8 (08):
[8] Hinaga S., 2009, P IPC APEX EXPO LAS
[9] The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method
Katirci, R.
Aktas, H.
Zontul, M.
[J]. TRANSACTIONS OF THE INSTITUTE OF METAL FINISHING, 2021, 99 (03): : 162 - 168
[10] Machine Learning Analysis of Ni/SiC Electrodeposition Using Association Rule Mining and Artificial Neural Network
Kilic, Aysegul
Yildirim, Ramazan
Eroglu, Damla
[J]. JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2021, 168 (06)

← 1 2 3 →