Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films

Yu Pu Yang; Hsiao Han Lo; Wei Lun Chen; Song Ho Wang; Te Yun Lu; Hsueh Er Chang; Peter J. Wang; Walter Lai; Yiin Kuen Fuh; Tomi T. Li

doi:10.1109/CSTIC52283.2021.9461536

Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films

Yu Pu Yang, Hsiao Han Lo, Wei Lun Chen, Song Ho Wang, Te Yun Lu, Hsueh Er Chang, Peter J. Wang, Walter Lai, Yiin Kuen Fuh, Tomi T. Li

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Plasma enhanced chemical vapor deposition (PECVD) is commonly known to be used in the field of silicon thin-film solar systems for the application of nanocrystalline silicon (nc-Si:H) film. The chemical deposition is a rather lengthy process, and it is difficult to determine the crystallization and crystalline phase of the thin film prior to X-ray diffraction (XRD) measurements. In this study, we are trying to analyze the spectral data collected by optical emission spectroscopy (OES) to find out there is any correlation between OES data and crystalline status. We used machine learning onto an in-situ detection tool to forecast this correlation. The collected large-scale OES spectral data obtained via principal component analysis (PCA) was used for the prediction of the crystalline phase in films without necessary experiments performed afterwards. Therefore, this method can be applicable to the field of thin film deposition for the detection of properties on thin films.

Original language	English
Title of host publication	China Semiconductor Technology International Conference 2021, CSTIC 2021
Editors	Cor Claeys, Steve X. Liang, Qinghuang Lin, Ru Huang, Hanming Wu, Peilin Song, Linyong Pang, Ying Zhang, Beichao Zhang, Xinping Xinping Qu, Cheng Zhuo, Hsiang-Lan Lung
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665449458
DOIs	https://doi.org/10.1109/CSTIC52283.2021.9461536
State	Published - 14 Mar 2021
Event	2021 China Semiconductor Technology International Conference, CSTIC 2021 - Shanghai, China Duration: 14 Mar 2021 → 15 Mar 2021

Publication series

Name	China Semiconductor Technology International Conference 2021, CSTIC 2021

Conference

Conference	2021 China Semiconductor Technology International Conference, CSTIC 2021
Country/Territory	China
City	Shanghai
Period	14/03/21 → 15/03/21

Access to Document

10.1109/CSTIC52283.2021.9461536

Cite this

Yang, Y. P., Lo, H. H., Chen, W. L., Wang, S. H., Lu, T. Y., Chang, H. E., Wang, P. J., Lai, W., Fuh, Y. K., & Li, T. T. (2021). Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films. In C. Claeys, S. X. Liang, Q. Lin, R. Huang, H. Wu, P. Song, L. Pang, Y. Zhang, B. Zhang, X. Xinping Qu, C. Zhuo, & H.-L. Lung (Eds.), China Semiconductor Technology International Conference 2021, CSTIC 2021 Article 9461536 (China Semiconductor Technology International Conference 2021, CSTIC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSTIC52283.2021.9461536

Yang, Yu Pu ; Lo, Hsiao Han ; Chen, Wei Lun et al. / Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films. China Semiconductor Technology International Conference 2021, CSTIC 2021. editor / Cor Claeys ; Steve X. Liang ; Qinghuang Lin ; Ru Huang ; Hanming Wu ; Peilin Song ; Linyong Pang ; Ying Zhang ; Beichao Zhang ; Xinping Xinping Qu ; Cheng Zhuo ; Hsiang-Lan Lung. Institute of Electrical and Electronics Engineers Inc., 2021. (China Semiconductor Technology International Conference 2021, CSTIC 2021).

@inproceedings{222c721f3a624a028a475412c5dd2bf7,

title = "Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films",

abstract = "Plasma enhanced chemical vapor deposition (PECVD) is commonly known to be used in the field of silicon thin-film solar systems for the application of nanocrystalline silicon (nc-Si:H) film. The chemical deposition is a rather lengthy process, and it is difficult to determine the crystallization and crystalline phase of the thin film prior to X-ray diffraction (XRD) measurements. In this study, we are trying to analyze the spectral data collected by optical emission spectroscopy (OES) to find out there is any correlation between OES data and crystalline status. We used machine learning onto an in-situ detection tool to forecast this correlation. The collected large-scale OES spectral data obtained via principal component analysis (PCA) was used for the prediction of the crystalline phase in films without necessary experiments performed afterwards. Therefore, this method can be applicable to the field of thin film deposition for the detection of properties on thin films.",

author = "Yang, {Yu Pu} and Lo, {Hsiao Han} and Chen, {Wei Lun} and Wang, {Song Ho} and Lu, {Te Yun} and Chang, {Hsueh Er} and Wang, {Peter J.} and Walter Lai and Fuh, {Yiin Kuen} and Li, {Tomi T.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 China Semiconductor Technology International Conference, CSTIC 2021 ; Conference date: 14-03-2021 Through 15-03-2021",

year = "2021",

month = mar,

day = "14",

doi = "10.1109/CSTIC52283.2021.9461536",

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series = "China Semiconductor Technology International Conference 2021, CSTIC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Cor Claeys and Liang, {Steve X.} and Qinghuang Lin and Ru Huang and Hanming Wu and Peilin Song and Linyong Pang and Ying Zhang and Beichao Zhang and {Xinping Qu}, Xinping and Cheng Zhuo and Hsiang-Lan Lung",

booktitle = "China Semiconductor Technology International Conference 2021, CSTIC 2021",

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Yang, YP, Lo, HH, Chen, WL, Wang, SH, Lu, TY, Chang, HE, Wang, PJ, Lai, W, Fuh, YK & Li, TT 2021, Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films. in C Claeys, SX Liang, Q Lin, R Huang, H Wu, P Song, L Pang, Y Zhang, B Zhang, X Xinping Qu, C Zhuo & H-L Lung (eds), China Semiconductor Technology International Conference 2021, CSTIC 2021., 9461536, China Semiconductor Technology International Conference 2021, CSTIC 2021, Institute of Electrical and Electronics Engineers Inc., 2021 China Semiconductor Technology International Conference, CSTIC 2021, Shanghai, China, 14/03/21. https://doi.org/10.1109/CSTIC52283.2021.9461536

Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films. / Yang, Yu Pu; Lo, Hsiao Han; Chen, Wei Lun et al.
China Semiconductor Technology International Conference 2021, CSTIC 2021. ed. / Cor Claeys; Steve X. Liang; Qinghuang Lin; Ru Huang; Hanming Wu; Peilin Song; Linyong Pang; Ying Zhang; Beichao Zhang; Xinping Xinping Qu; Cheng Zhuo; Hsiang-Lan Lung. Institute of Electrical and Electronics Engineers Inc., 2021. 9461536 (China Semiconductor Technology International Conference 2021, CSTIC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films

AU - Yang, Yu Pu

AU - Lo, Hsiao Han

AU - Chen, Wei Lun

AU - Wang, Song Ho

AU - Lu, Te Yun

AU - Chang, Hsueh Er

AU - Wang, Peter J.

AU - Lai, Walter

AU - Fuh, Yiin Kuen

AU - Li, Tomi T.

PY - 2021/3/14

Y1 - 2021/3/14

N2 - Plasma enhanced chemical vapor deposition (PECVD) is commonly known to be used in the field of silicon thin-film solar systems for the application of nanocrystalline silicon (nc-Si:H) film. The chemical deposition is a rather lengthy process, and it is difficult to determine the crystallization and crystalline phase of the thin film prior to X-ray diffraction (XRD) measurements. In this study, we are trying to analyze the spectral data collected by optical emission spectroscopy (OES) to find out there is any correlation between OES data and crystalline status. We used machine learning onto an in-situ detection tool to forecast this correlation. The collected large-scale OES spectral data obtained via principal component analysis (PCA) was used for the prediction of the crystalline phase in films without necessary experiments performed afterwards. Therefore, this method can be applicable to the field of thin film deposition for the detection of properties on thin films.

AB - Plasma enhanced chemical vapor deposition (PECVD) is commonly known to be used in the field of silicon thin-film solar systems for the application of nanocrystalline silicon (nc-Si:H) film. The chemical deposition is a rather lengthy process, and it is difficult to determine the crystallization and crystalline phase of the thin film prior to X-ray diffraction (XRD) measurements. In this study, we are trying to analyze the spectral data collected by optical emission spectroscopy (OES) to find out there is any correlation between OES data and crystalline status. We used machine learning onto an in-situ detection tool to forecast this correlation. The collected large-scale OES spectral data obtained via principal component analysis (PCA) was used for the prediction of the crystalline phase in films without necessary experiments performed afterwards. Therefore, this method can be applicable to the field of thin film deposition for the detection of properties on thin films.

UR - http://www.scopus.com/inward/record.url?scp=85113653050&partnerID=8YFLogxK

U2 - 10.1109/CSTIC52283.2021.9461536

DO - 10.1109/CSTIC52283.2021.9461536

M3 - 會議論文篇章

AN - SCOPUS:85113653050

T3 - China Semiconductor Technology International Conference 2021, CSTIC 2021

BT - China Semiconductor Technology International Conference 2021, CSTIC 2021

A2 - Claeys, Cor

A2 - Liang, Steve X.

A2 - Lin, Qinghuang

A2 - Huang, Ru

A2 - Wu, Hanming

A2 - Song, Peilin

A2 - Pang, Linyong

A2 - Zhang, Ying

A2 - Zhang, Beichao

A2 - Xinping Qu, Xinping

A2 - Zhuo, Cheng

A2 - Lung, Hsiang-Lan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 China Semiconductor Technology International Conference, CSTIC 2021

Y2 - 14 March 2021 through 15 March 2021

ER -

Yang YP, Lo HH, Chen WL, Wang SH, Lu TY, Chang HE et al. Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films. In Claeys C, Liang SX, Lin Q, Huang R, Wu H, Song P, Pang L, Zhang Y, Zhang B, Xinping Qu X, Zhuo C, Lung HL, editors, China Semiconductor Technology International Conference 2021, CSTIC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9461536. (China Semiconductor Technology International Conference 2021, CSTIC 2021). doi: 10.1109/CSTIC52283.2021.9461536

Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films

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