光刻机工件台宏动三自由度建模及自适应神经网络控制

王一光; 陈兴林; 李晓杰

doi:10.3788/OPE.20152301.0132

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光刻机工件台宏动三自由度建模及自适应神经网络控制

微纳技术与精密机械 | 更新时间：2020-08-13

- 光刻机工件台宏动三自由度建模及自适应神经网络控制
- Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage
- 光学精密工程 2015年23卷第1期页码：132-140
- 作者机构：
  
  1. 哈尔滨工业大学航天学院,黑龙江哈尔滨,150001
  2. 黑龙江省工业技术研究院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
  
  国家973重点基础研究发展计划资助项目(No.973-10007.07-LB7)();国家重大科技专项资助项目(No.2009ZX02207)()
- DOI：10.3788/OPE.20152301.0132
  中图分类号： TP273.2;TN305.7
- 收稿日期：2014-05-04，
  
  修回日期：2014-06-04，
  
  纸质出版日期：2015-01-25
- 稿件说明：
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王一光, 陈兴林, 李晓杰. 光刻机工件台宏动三自由度建模及自适应神经网络控制[J]. 光学精密工程, 2015,23(1): 132-140

WANG Yi-guang, CHEN Xing-lin, LI Xiao-jie. Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 132-140
王一光, 陈兴林, 李晓杰. 光刻机工件台宏动三自由度建模及自适应神经网络控制[J]. 光学精密工程, 2015,23(1): 132-140 DOI： 10.3788/OPE.20152301.0132.

WANG Yi-guang, CHEN Xing-lin, LI Xiao-jie. Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 132-140 DOI： 10.3788/OPE.20152301.0132.

摘要

提出了一种工件台宏动三自由度建模方法以解决光刻机工件台宏动部分在

和

方向的运动耦合问题并实现它的超精密长行程微米精度的跟踪定位.该建模方法将

方向电机的偏转角度作为被控对象并且在模型中包含了耦合效应对

方向运动的影响.基于此模型提出了一种自适应神经网络控制策略

该策略采用径向基函数(Radial Basis Function

RBF)神经网络对模型参数信息及外界非线性扰动进行实时在线估计

以减小未建模动态、电机齿槽力波动、端部效应、摩擦等扰动对控制系统性能的影响.通过对控制策略的理论推导和稳定性分析

保证了闭环控制系统的收敛性.最后在光刻机工件台上进行了

曲线跟踪定位试验

验证了宏动三自由度建模方法和控制策略的效果.试验结果显示:

和

方向的位置跟踪误差均小于3 μm

方向电机偏转角度小于1 μrad

满足工件台宏动部分跟踪定位精度的要求.

Abstract

A three degrees of freedom modeling method for a long-stroke wafer stage in lithography was proposed to solve the

X-Y

coupling problem of the long-stroke wafer stage and to achieve ultra-precision tracking with micron accuracy. In the modeling method

the rotation angle of

linear motor was considered as one of the controlled objects and the coupling effect on the moving in

direction was involved in the model. Then an adaptive neural network control method was presented based on the proposed model. The Radial Basis Function (RBF) neural network was used to estimate the model information and external nonlinear disturbances real-time online and to reduce the influences of unmodeled dynamics

cogging forces

end effect and friction on the control system. With the theoretical derivation and stability analysis

the convergence of the closed-loop system was guaranteed. Finally

the effectiveness of the modeling and the control method were verified by a

-curve tracking experiment on the actual long-stroke wafer stage of the lithography. The experiment results show that the tracking errors of the

and

linear motors are less than 3 μm and the rotation angle of

motor is less than 1 μrad. The tracking errors meet the design requirements.

关键词

Keywords

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