Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes

doi:10.1016/j.cjche.2015.11.013

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1958-1964.DOI: 10.1016/j.cjche.2015.11.013

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes

Pei Sun, Lei Xie, Junghui Chen

State Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

收稿日期:2015-06-03 修回日期:2015-07-19 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Lei Xie, Junghui Chen
基金资助:
Supported by the National High Technology Research and Development Program of China (2014AA041803) and the National Natural Science Foundation of China (61320106009).

Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes

Pei Sun, Lei Xie, Junghui Chen

State Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Received:2015-06-03 Revised:2015-07-19 Online:2015-12-28 Published:2016-01-19
Contact: Lei Xie, Junghui Chen
Supported by:
Supported by the National High Technology Research and Development Program of China (2014AA041803) and the National Natural Science Foundation of China (61320106009).

摘要/Abstract

摘要： Low pressure chemical vapor deposition (LPCVD) is one of themost important processes during semiconductor manufacturing. However, the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process. Besides, due to the properties of this process, the reliability of the model must be taken into consideration when optimizing the MVs. In this work, an optimal design strategy based on the self-learning Gaussian processmodel (GPM)is proposed to control this kind of spatial batch process. The GPMis utilized as the internalmodel to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data. Unlike the conventional model based design, the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent. Besides, the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties. The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

关键词: Batchwise, LPCVD, Transport processes, Spatial distribution, Gaussian process model, Optimal design

Abstract: Low pressure chemical vapor deposition (LPCVD) is one of themost important processes during semiconductor manufacturing. However, the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process. Besides, due to the properties of this process, the reliability of the model must be taken into consideration when optimizing the MVs. In this work, an optimal design strategy based on the self-learning Gaussian processmodel (GPM)is proposed to control this kind of spatial batch process. The GPMis utilized as the internalmodel to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data. Unlike the conventional model based design, the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent. Besides, the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties. The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

Key words: Batchwise, LPCVD, Transport processes, Spatial distribution, Gaussian process model, Optimal design

Pei Sun, Lei Xie, Junghui Chen. Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1958-1964.

Pei Sun, Lei Xie, Junghui Chen. Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes[J]. Chin.J.Chem.Eng., 2015, 23(12): 1958-1964.

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Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes

Spatial batch optimal design based on self-learning gaussian process models for LPCVD processes

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