An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes

Zhen Yin Annie Chen; Chun Cheng Lin; Ke Wen Lu; Hui Hsin Chin; Der Jiunn Deng

doi:10.1109/IECON51785.2023.10312119

An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes

Zhen Yin Annie Chen, Chun Cheng Lin, Ke Wen Lu, Hui Hsin Chin, Der Jiunn Deng^*

^*Corresponding author for this work

Department of Industrial Engineering and Management

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

Abstract

It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10312119
State	Published - 2023
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

Keywords

Bayesian optimization
chemical vapor deposition process
process recipe parameters

Access to Document

10.1109/IECON51785.2023.10312119

Cite this

Chen, Z. Y. A., Lin, C. C., Lu, K. W., Chin, H. H., & Deng, D. J. (2023). An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes. In IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference)). IEEE Computer Society. https://doi.org/10.1109/IECON51785.2023.10312119

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title = "An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes",

abstract = "It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.",

keywords = "Bayesian optimization, chemical vapor deposition process, process recipe parameters",

author = "Chen, {Zhen Yin Annie} and Lin, {Chun Cheng} and Lu, {Ke Wen} and Chin, {Hui Hsin} and Deng, {Der Jiunn}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10312119",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

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booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

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Chen, ZYA, Lin, CC, Lu, KW, Chin, HH & Deng, DJ 2023, An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10312119

An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes. / Chen, Zhen Yin Annie; Lin, Chun Cheng; Lu, Ke Wen et al.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

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

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T1 - An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes

AU - Chen, Zhen Yin Annie

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AU - Lu, Ke Wen

AU - Chin, Hui Hsin

AU - Deng, Der Jiunn

PY - 2023

Y1 - 2023

N2 - It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

AB - It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

KW - Bayesian optimization

KW - chemical vapor deposition process

KW - process recipe parameters

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DO - 10.1109/IECON51785.2023.10312119

M3 - Conference contribution

AN - SCOPUS:85179522456

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes

Abstract

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Keywords

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