Model-based feedforward and feedback control of the verticalgradient-freeze-crystal growth process using distributed parameter methods

使用分布参数方法对垂直梯度冷冻晶体生长过程进行基于模型的前馈和反馈控制

基本信息

项目摘要

An important process for the production of mono crystalline materials is the vertical gradient freeze (VGF) process. Here, poly-crystalline material is molten in a crucible. Then crystallization starts from the bottom into upward direction until the complete melt is solidified. For this purpose the temperature field surrounding the crucible is manipulated in such a way that the melting point isotherm travels slowly from the bottom to the top of the crucible. The required controls are realized by resistance heaters placed around the crucible. In order to drive the process in an appropriate manner information about the actual state is required, especially of the crystallization rate. However, this cannot be captured. This is due to high process temperature, the reactivity of the used materials and the requirements with respect to the purity of the product preventing any measurement devices to be placed within the crucible. Only the temperatures at the resistance heaters can be measured. Because of this it is not possible to establish a real closed loop control. It is only feed forward black box controlled. From a control theoretic point of view the VGF process is a typical candidate of a distributed parameter system with free boundaries (the solid-liquid-interface) and lumped control input (the heaters). The objective of the proposed project is to develop theoretical and practical methods for a real feedback control of the process. This also includes methods for the reconstruction of the system state from available or newly introduced measurements. For this purpose it is planned to use distributed parameter methods that have been developed by the Institute of Control Theory (TU Dresden) as one of the leading institutes in this subject for several years now. Up to now the focus was mainly on the feedforward control and the parameter identification of such systems. The work will be accompanied with intensive work on modeling of the VGF process by the Institute of Crystal Growth Berlin (IKZ). These models are required for controller and observer design as well as for the validation of the developed control and reconstruction algorithms so experimental effort can be reduced. Here, the challenge is to find mathematical models of the process which on the one hand side are sufficiently precise for control and on the other hand side are real time capable. Furthermore they must meet the structural requirements given by the selected distributed parameter methods. Although the control methods developed during the project will found a highly sophisticated theoretical framework the practical meaningfulness will be guaranteed by experimental tests at the VGF growth plants.
生产单晶材料的一个重要工艺是垂直梯度冷冻(VGF)工艺。这里,多晶材料在坩埚中熔化。然后结晶从底部开始向上,直至完全熔体凝固。为此,以熔点等温线从坩埚底部缓慢移动到顶部的方式控制坩埚周围的温度场。所需的控制是通过放置在坩埚周围的电阻加热器实现的。为了以适当的方式驱动该过程,需要有关实际状态的信息,尤其是结晶速率的信息。然而,这无法被捕获。这是由于高工艺温度、所用材料的反应性以及对产品纯度的要求,导致无法将任何测量设备放置在坩埚内。只能测量电阻加热器的温度。因此不可能建立真正的闭环控制。它仅由前馈黑匣子控制。从控制理论的角度来看,VGF 过程是具有自由边界(固液界面)和集总控制输入(加热器)的分布式参数系统的典型候选者。所提议项目的目标是开发用于过程的真实反馈控制的理论和实践方法。这还包括根据可用的或新引入的测量重建系统状态的方法。为此,计划使用控制理论研究所(德累斯顿工业大学)多年来开发的分布式参数方法,该研究所是该学科的领先研究所之一。到目前为止,重点主要集中在此类系统的前馈控制和参数识别上。这项工作将伴随着柏林晶体生长研究所 (IKZ) 对 VGF 过程建模的深入研究。这些模型是控制器和观测器设计以及开发的控制和重建算法验证所必需的,因此可以减少实验工作。这里的挑战是找到该过程的数学模型,该模型一方面足够精确以进行控制,另一方面又具有实时能力。此外,它们必须满足所选分布参数方法给出的结构要求。尽管该项目期间开发的控制方法将建立一个高度复杂的理论框架,但实际意义将通过 VGF 生长植物的实验测试来保证。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Approximation and implementation of transformation based feedback laws for distributed parameter systems
分布式参数系统基于变换的反馈律的近似和实现
  • DOI:
    10.1002/pamm.201710360
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ecklebe; S.; M. Riesmeier und F. Woittennek
  • 通讯作者:
    M. Riesmeier und F. Woittennek
Control of the Vertical Gradient Freeze crystal growth process via backstepping
通过反步控制垂直梯度冻结晶体生长过程
  • DOI:
    10.1016/j.ifacol.2020.12.1537
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ecklebe; S.; F. Woittennek und J. Winkler
  • 通讯作者:
    F. Woittennek und J. Winkler
Toward Model-Based Control of the Vertical Gradient Freeze Crystal Growth Process
垂直梯度冷冻晶体生长过程的基于模型的控制
Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models
使用人工神经网络和高斯过程模型优化磁驱动硅定向凝固
  • DOI:
    10.1016/j.jcrysgro.2017.05.007
  • 发表时间:
    2017-08-01
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    N. Dropka;M. Holeňa
  • 通讯作者:
    M. Holeňa
Fast forecasting of VGF crystal growth process by dynamic neural networks
动态神经网络快速预测VGF晶体生长过程
  • DOI:
    10.1016/j.jcrysgro.2019.05.022
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    Dropka; N.; M. Holena; S. Ecklebe; Chr. Frank
  • 通讯作者:
    Chr. Frank
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Dr. Christiane Frank-Rotsch其他文献

Dr. Christiane Frank-Rotsch的其他文献

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