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CMP过程多变量免疫预测R2R控制方法
Alternative TitleMultivariable immune predictive R2R control method for CMP process
王亮; 胡静涛
Department信息服务与智能控制技术研究室
Source Publication仪器仪表学报
ISSN0254-3087
2012
Volume33Issue:11Pages:2586-2593
Indexed ByEI ; CSCD
EI Accession number20130215883844
CSCD IDCSCD:4717640
Contribution Rank1
Funding Organization国家科技重大专项(2009ZX02008-003、国家科技重大专项( 2009ZX02001-005、国家自然科学青年基金(61104093 )、973 计划前期研究专项课题(2010CB334705) 资助项目
Keyword化学机械研磨 R2r控制 最小二乘支持向量机 贝叶斯证据框架 克隆选择 预测控制
Abstract为了解决多输入多输出和产品质量不易在线测量的化学机械研磨(chemical mechanical polishing, CMP) 过程R2R(nm-to-run) 控制的难题,提出了一种基于贝叶斯最小二乘支持向量机(Bayes least squares support vector machine, BLS-SVM) 预测模型和克隆选择免疫多目标滚动优化算法的CMP 过程多变量R2R 预测控制器BSVMPR2R。由LS-SVM 和贝叶斯证据框架(Bayes evidence framework , BEF) 方法分别构建材料去除率(material removal rate , MRR) 和品圆内非均匀度(witmn-wafer nonwüformity , WIWNU) 的BLS-SVM 预测模型,解决了线性预测模型的失配问题;通过预测误差对后续批次过程扰动和漂移进行在线估计实现反馈校正,提高了预测模型精度,将多变量控制j问题转化为基于2个预测模型的多目标优化问题,由克隆选择免疫多目标滚动优化算法求解最优控制律提高了控制精度。仿真结果表明, BSVMPR2R 控制器的性能优于双指数加权移动平均(double exponential weighted moving average , dEWMA) 多变量控制器,抑制了CMP 过程扰动和漂移的影响,显著降低了MRR 和WIWNU 的均方根误差。
Other AbstractIn order to solve the R2R(run-to-run) control problem in chemical mechanical polishing (CMP) process with the features of multi-input & multi-output and difficulty of product quality online measurement, a CMP process multivariable predictive R2R controller named BSVMPR2R based on Bayes least squares support vector machine (BLS-SVM) prediction model and the clonal selection immune multi-objective receding horizon optimization algorithm are proposed.LS-SVM and Bayes evidence framework(BEF) methods are used to build the BLS-SVM prediction models of material removal rate (MRR) and within-wafer nonuniformity (WIWNU), respectively, which solve the mismatch problem of linear prediction model.The prediction errors are used to online estimate the next run disturbances and drifts, achieve feedback correction and improve the prediction model accuracy.Multivariable control problem is transformed into multi-objective optimization problem based on the two prediction models, and clonal selection immune multi-objective receding horizon optimization algorithm is used to solve the optimal control law, which improves the control precision.Simulation results illustrate that the performance of BSVMPR2R controller is superior to that of double exponential weighted moving average (dEWMA) multivariable controller, the effects of CMP process disturbances and drifts are restrained, and the RMSEs of MRR and WIWNU are reduced significantly.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10321
Collection信息服务与智能控制技术研究室
Affiliation1.中国科学院沈阳自动化研究所工业信息学重点实验室
2.中国科学院研究生院
3.沈阳化工大学
Recommended Citation
GB/T 7714
王亮,胡静涛. CMP过程多变量免疫预测R2R控制方法[J]. 仪器仪表学报,2012,33(11):2586-2593.
APA 王亮,&胡静涛.(2012).CMP过程多变量免疫预测R2R控制方法.仪器仪表学报,33(11),2586-2593.
MLA 王亮,et al."CMP过程多变量免疫预测R2R控制方法".仪器仪表学报 33.11(2012):2586-2593.
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