基于变折扣因子EWMA的混合产品批间控制方法

批间(run-to-run,简称R2R)控制现今已被广泛用于半导体生产行业.指数加权移动平均(exponet weighted moving average.EWMA)是R2R控制的一种重要算法.折扣因子是EWMA控制期的主要参数.本文在模型中考虑了实际生产过程中混合产品少量多样的特点,引入了基于产品的变折扣因子EWMA控制算法,解决了产品切换时制程输出收敛速度过慢的问题.变折扣因子的引入提高了制程输出的响应速率而并不影响制程输出的稳定性.对实际过程的模拟仿真检验了该控制算法的可行性和优越性.     

半导体混合产品批间控制的理论与实验研究

提出了用一种半导体工业用双产品模型来研究基于机台和基于产品的控制方法.在此基础上,得到了闭环系统的输出,评估了控制性能.由于NH3的挥发类似于半导体工业的机台漂移,因此对纳米级SiO2粒子进行了实验研究.在氨溶液挥发的影响下制造不同大小的SiO2粒子,以模拟在机台干扰下生产不同产品.实验研究证实了理论结果,即基于机台的方法有时不稳定,而基于产品的方法是稳定的.     

等离子体刻蚀过程的APC技术研究进展

先进过程控制(APC)是一个多层次的控制系统,其包含了实时的设备及工艺控制和非实时的RtR控制.APC引入等离子体刻蚀过程控制可极大提高刻蚀机的使用效率.针对等离子体刻蚀过程,本文分别从实时控制和RtR控制两个不同层次展开了论述,概述了RtR控制器中所使用的线性回归模型和神经网络模型.最后,讨论了APC技术的发展趋势.     

应用群能量恒定粒子群优化算法的批次流加发酵过程批次间优化(英文)

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.     

含随机测量时延的EWMA批间控制器设计

批间控制(RtR)是半导体晶圆生产过程控制的有效算法. 然而, 受测量手段与测量成本的限制, 难以实时检 测晶圆的品质数据, 即: 存在一定的测量时延, 通常该测量时延是随机, 时变的, 且直接影响批间控制器的性能. 为 此, 本文基于指数加权移动平均(EWMA)算法, 提出一种含随机测量时延的扰动估计方法. 在分析测量概率的基础 上, 建立包含测量时延概率的扰动估计表达式; 并采用期望最大化(EM)算法估计该测量时延的概率; 然后分析系统 可能存在的静差项, 给出相应的补偿算法; 最后讨论系统的稳定性. 仿真实例验证所提算法的有效性.     

基于神经网络的CMP过程智能R2R预测控制

针对化学机械研磨(CMP)过程非线性、时变和不易在线测量的特性,提出了基于径向基函数(RBF)神经网络和微粒群(PSO)算法的CMP过程run-to-run(R2R)预测控制器NNPR2R。首先通过样本数据用减聚类算法和最小二乘法构建CMP过程的RBF神经网络预测模型,解决了复杂CMP过程难以建立精确数学模型的难题和提高了预测模型的精度。然后通过PSO算法滚动优化求取控制律,解决了基于导数的优化技术易于陷入局部最优的问题并提高了控制精度。仿真结果表明,CMP过程NNPR2R控制器的性能优于常规的EWMA方法,有效抑制了过程漂移和减小了不同批次间产品的差异,显著降低了材料去除率(MRR)的均方根误差。     

基于LS-SVM的光刻过程R2R预测控制方法

针对光刻过程非线性、时变和产品质量不易在线测量的特性,提出了一种基于最小二乘支持向量机预测模型和微粒群滚动优化方法的批次控制预测控制器。通过历史批次样本数据构建光刻过程的最小二乘支持向量机预测模型,解决了复杂光刻过程难以建立精确数学模型的难题,提高了预测模型的精度。通过预测误差的反馈校正和微粒群滚动优化算法求解最优控制律,提高了控制精度。性能分析结果表明,与指数加权移动平均方法及非线性模型预测控制方法相比较,批次控制预测控制器控制器减小了不同批次关键尺寸输出的差异,显著降低了关键尺寸输出的均方根误差,有效抑制了过程扰动影响。     

A run-to-run controller for product surface quality improvement

In semiconductor manufacturing, the surface quality of silicon wafers has a significant impact on the subsequent processes that produce devices using the wafers as a component. The surface quality of a wafer is characterised by a two-dimensional (2-D) data structure: the geometric requirement for the wafer surface is smooth and flat and the thickness should fall within certain specification limits. Therefore, both low deviation and high uniformity are desirable for control over the wafer quality. In this work, we develop a run-to-run control algorithm for improving wafer quality. Considering the unique 2-D data structure, we first construct a model that encompasses the spatial correlation of the observations on the wafer surface to link the wafer quality with the process variables, and subsequently develop a recursive algorithm to generate optimal set points for the controllable factors. More specifically, a Gaussian-Kriging model is used to characterise the spatial dependence of the thickness measures of the wafer and a recursive least square method is employed to update the estimates of the model parameters. The performance of the new controller is studied via simulation and compared with existing controllers, which demonstrates that the newly proposed controller can effectively reduce the surface variations of the silicon wafers.     

Performance assessment of run-to-run control in semiconductor manufacturing based on IMC framework

The objective of this paper is to propose a universal methodology for performance assessment of run-to-run control in semiconductor manufacturing. The slope of the linear semiconductor process model is assumed to be known or subjected to mild plant/model mismatch. Based on an internal model control framework, analytical expressions of minimum variance performance (MVP) and best achievable performance (BAP) for a series of run-to-run control schemes are derived. In the methodology, closed-loop identification is utilised as the first step to estimate the noise dynamics via routine operating data, and numerical optimisation is employed as a second step to calculate the best achievable performance bounds of the run-to-run control loops. The validity of the methodology is justified by examples of performance assessment for EWMA control, double EWMA control and RLS-LT control, even under circumstances where the processes encounter model mismatch, metrology delay and more sophisticated noises. Several essential characteristics of run-to-run control are discovered by performance assessment, and valuable advice is offered to process engineers for improving the run-to-run control performance. Furthermore, a useful application example for online performance monitoring and optimal tuning of run-to-run controller demonstrates the advantage of the methodology.     

Self-tuning run to run optimization of fed-batch processes using unfold-PLS

Unfold Partial-Least Squares (u-PLS) is a modeling method successfully applied to batch-process monitoring and end quality prediction. This method is integrated in a self-tuning optimization algorithm, based on extremum-seeking control. The optimization is driven by the gradient obtained by means of an adaptive u-PLS model. Since this is an empirical model, no first-principles based knowledge of the process is necessary. Heuristic rules are used to constrain the gradient taking into account nonlinearity and unknown causes of variability. Extensions to model the variability in initial conditions, to optimize several performance indices, and to handle inequality constraints are presented. The optimization algorithm is tested on a complex comprehensive simulated-process model for the fed-batch cultivation of Sacharomyces cerevisiae. Results show the performance and versatility of the proposed approach, as well as its robustness to process changes. © 2007 American Institute of Chemical Engineers AIChE J, 2007