Abstract
The goal of this study was to investigate how changing the pulsed frequency affects the deposition process and correlates with AlN film properties. The resulting films were then characterized in terms of their crystallinity, microstructure, and surface roughness to identify any correlations with the pulsed frequency. This approach was used to determine the optimal pulsed conditions for film deposition. Each dataset spans the wavelength range of 190nm to 850nm, comprising 1,900 features. Following data collection, we employed traditional ensemble learning methods (Random Forest), tree-based gradient boosting (Categorical Boosting), and the improved gradient-boosted algorithm (Histogram Gradient Boosting), for predicting the quality of thin films. This analysis aimed to clarify which method excels in handling semiconductor process OES data to obtain an optimal processing pulsed frequency on reactive pulsed DC sputtering of aluminum nitride films.
Original language | English |
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Title of host publication | 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 |
Editors | Cor Claeys, Beichao Zhang, Bin Yu, Ru Huang, Xiaowei Li, Steve X. Liang, Jianshi Tang, Hsiang-Lan Lung, Linyong Pang, Weikang Qian, Xinping Qu, Xiaoping Shi, Ying Zhang |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350362190 |
DOIs | |
State | Published - 2024 |
Event | 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 - Shanghai, China Duration: 17 Mar 2024 → 18 Mar 2024 |
Publication series
Name | 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 |
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Conference
Conference | 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 |
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Country/Territory | China |
City | Shanghai |
Period | 17/03/24 → 18/03/24 |
Keywords
- aluminum nitride (AlN)
- Categorical Boosting (CatBoost)
- Histogram-Based Gradient Boosting (HGB)
- machine learning
- optical emission spectroscopy (OES)
- pulsed frequency
- Random Forest
- Reactive pulsed DC magnetron sputtering
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Tseng, X. L., Chen, Y. S., Chen, H. F., Lo, H. H., Wang, P. J., Dai, Y. M., Fuh, Y. K. (2024). A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films. In C. Claeys, B. Zhang, B. Yu, R. Huang, X. Li, S. X. Liang, J. Tang, H.-L. Lung, L. Pang, W. Qian, X. Qu, X. Shi, & Y. Zhang (Eds.), 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 (2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSTIC61820.2024.10532082
Tseng, Xue Li ; Chen, Yu Shin ; Chen, Hsuan Fan et al. / A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films. 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024. editor / Cor Claeys ; Beichao Zhang ; Bin Yu ; Ru Huang ; Xiaowei Li ; Steve X. Liang ; Jianshi Tang ; Hsiang-Lan Lung ; Linyong Pang ; Weikang Qian ; Xinping Qu ; Xiaoping Shi ; Ying Zhang. Institute of Electrical and Electronics Engineers Inc., 2024. (2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024).
@inproceedings{54dfc84b06c04c57a2bbab2094c575fb,
title = "A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films",
abstract = "The goal of this study was to investigate how changing the pulsed frequency affects the deposition process and correlates with AlN film properties. The resulting films were then characterized in terms of their crystallinity, microstructure, and surface roughness to identify any correlations with the pulsed frequency. This approach was used to determine the optimal pulsed conditions for film deposition. Each dataset spans the wavelength range of 190nm to 850nm, comprising 1,900 features. Following data collection, we employed traditional ensemble learning methods (Random Forest), tree-based gradient boosting (Categorical Boosting), and the improved gradient-boosted algorithm (Histogram Gradient Boosting), for predicting the quality of thin films. This analysis aimed to clarify which method excels in handling semiconductor process OES data to obtain an optimal processing pulsed frequency on reactive pulsed DC sputtering of aluminum nitride films.",
keywords = "aluminum nitride (AlN), Categorical Boosting (CatBoost), Histogram-Based Gradient Boosting (HGB), machine learning, optical emission spectroscopy (OES), pulsed frequency, Random Forest, Reactive pulsed DC magnetron sputtering",
author = "Tseng, {Xue Li} and Chen, {Yu Shin} and Chen, {Hsuan Fan} and Lo, {Hsiao Han} and Wang, {Peter J.} and Dai, {Yu Min} and Fuh, {Yiin Kuen} and Ting-Tung Li",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024 ; Conference date: 17-03-2024 Through 18-03-2024",
year = "2024",
doi = "10.1109/CSTIC61820.2024.10532082",
language = "???core.languages.en_GB???",
series = "2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Cor Claeys and Beichao Zhang and Bin Yu and Ru Huang and Xiaowei Li and Liang, {Steve X.} and Jianshi Tang and Hsiang-Lan Lung and Linyong Pang and Weikang Qian and Xinping Qu and Xiaoping Shi and Ying Zhang",
booktitle = "2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024",
}
Tseng, XL, Chen, YS, Chen, HF, Lo, HH, Wang, PJ, Dai, YM, Fuh, YK 2024, A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films. in C Claeys, B Zhang, B Yu, R Huang, X Li, SX Liang, J Tang, H-L Lung, L Pang, W Qian, X Qu, X Shi & Y Zhang (eds), 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024. 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024, Institute of Electrical and Electronics Engineers Inc., 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024, Shanghai, China, 17/03/24. https://doi.org/10.1109/CSTIC61820.2024.10532082
A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films. / Tseng, Xue Li; Chen, Yu Shin; Chen, Hsuan Fan et al.
2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024. ed. / Cor Claeys; Beichao Zhang; Bin Yu; Ru Huang; Xiaowei Li; Steve X. Liang; Jianshi Tang; Hsiang-Lan Lung; Linyong Pang; Weikang Qian; Xinping Qu; Xiaoping Shi; Ying Zhang. Institute of Electrical and Electronics Engineers Inc., 2024. (2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
TY - GEN
T1 - A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films
AU - Tseng, Xue Li
AU - Chen, Yu Shin
AU - Chen, Hsuan Fan
AU - Lo, Hsiao Han
AU - Wang, Peter J.
AU - Dai, Yu Min
AU - Fuh, Yiin Kuen
AU - Li, Ting-Tung
N1 - Publisher Copyright:© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The goal of this study was to investigate how changing the pulsed frequency affects the deposition process and correlates with AlN film properties. The resulting films were then characterized in terms of their crystallinity, microstructure, and surface roughness to identify any correlations with the pulsed frequency. This approach was used to determine the optimal pulsed conditions for film deposition. Each dataset spans the wavelength range of 190nm to 850nm, comprising 1,900 features. Following data collection, we employed traditional ensemble learning methods (Random Forest), tree-based gradient boosting (Categorical Boosting), and the improved gradient-boosted algorithm (Histogram Gradient Boosting), for predicting the quality of thin films. This analysis aimed to clarify which method excels in handling semiconductor process OES data to obtain an optimal processing pulsed frequency on reactive pulsed DC sputtering of aluminum nitride films.
AB - The goal of this study was to investigate how changing the pulsed frequency affects the deposition process and correlates with AlN film properties. The resulting films were then characterized in terms of their crystallinity, microstructure, and surface roughness to identify any correlations with the pulsed frequency. This approach was used to determine the optimal pulsed conditions for film deposition. Each dataset spans the wavelength range of 190nm to 850nm, comprising 1,900 features. Following data collection, we employed traditional ensemble learning methods (Random Forest), tree-based gradient boosting (Categorical Boosting), and the improved gradient-boosted algorithm (Histogram Gradient Boosting), for predicting the quality of thin films. This analysis aimed to clarify which method excels in handling semiconductor process OES data to obtain an optimal processing pulsed frequency on reactive pulsed DC sputtering of aluminum nitride films.
KW - aluminum nitride (AlN)
KW - Categorical Boosting (CatBoost)
KW - Histogram-Based Gradient Boosting (HGB)
KW - machine learning
KW - optical emission spectroscopy (OES)
KW - pulsed frequency
KW - Random Forest
KW - Reactive pulsed DC magnetron sputtering
UR - http://www.scopus.com/inward/record.url?scp=85195114697&partnerID=8YFLogxK
U2 - 10.1109/CSTIC61820.2024.10532082
DO - 10.1109/CSTIC61820.2024.10532082
M3 - 會議論文篇章
AN - SCOPUS:85195114697
T3 - 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024
BT - 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024
A2 - Claeys, Cor
A2 - Zhang, Beichao
A2 - Yu, Bin
A2 - Huang, Ru
A2 - Li, Xiaowei
A2 - Liang, Steve X.
A2 - Tang, Jianshi
A2 - Lung, Hsiang-Lan
A2 - Pang, Linyong
A2 - Qian, Weikang
A2 - Qu, Xinping
A2 - Shi, Xiaoping
A2 - Zhang, Ying
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024
Y2 - 17 March 2024 through 18 March 2024
ER -
Tseng XL, Chen YS, Chen HF, Lo HH, Wang PJ, Dai YM et al. A Machine Learning Study to Obtain an Optimal Processing Pulsed Frequency on Reactive Pulsed DC Sputtering of Aluminum Nitride Films. In Claeys C, Zhang B, Yu B, Huang R, Li X, Liang SX, Tang J, Lung HL, Pang L, Qian W, Qu X, Shi X, Zhang Y, editors, 2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (2024 Conference of Science and Technology for Integrated Circuits, CSTIC 2024). doi: 10.1109/CSTIC61820.2024.10532082