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^*

^*此作品的通信作者

工業工程與管理學系

研究成果: Conference contribution › 同行評審

摘要

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.

原文	English
主出版物標題	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
發行者	IEEE Computer Society
ISBN（電子）	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10312119
出版狀態	Published - 2023
事件	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, 新加坡持續時間: 16 10月 2023 → 19 10月 2023

出版系列

名字	IECON Proceedings (Industrial Electronics Conference)
ISSN（列印）	2162-4704
ISSN（電子）	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
國家/地區	新加坡
城市	Singapore
期間	16/10/23 → 19/10/23

存取文件

10.1109/IECON51785.2023.10312119

其它文件與鏈接

Link to publication in Scopus

引用此

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. 於 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

@inproceedings{1e237a22cdc540fcb9d49b8845ac2ec8,

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)",

publisher = "IEEE Computer Society",

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

address = "美國",

}

Chen, ZYA, Lin, CC, Lu, KW, Chin, HH & Deng, DJ 2023, An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes. 於 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, 新加坡, 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 等.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

研究成果: Conference contribution › 同行評審

TY - GEN

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

AU - Chen, Zhen Yin Annie

AU - Lin, Chun Cheng

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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U2 - 10.1109/IECON51785.2023.10312119

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

摘要

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Conference

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