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Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity.

机译：基于传感器的实时过程监控，用于具有非线性和非平稳性的超精密制造过程。

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摘要

This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation. The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.;In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.;Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.;Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications.
机译：本文研究了超精密制造过程中实时过程监控的方法，特别是化学机械平面化（CMP）和超精密加工（UPM）。这项研究的三个主要组成部分如下：（1）开发一种预测模型方法以及早发现过程异常/变化点，（2）设计可以捕获CMP和UPM的非高斯和非平稳特性的方法过程；（3）集成多个传感器数据以实时做出更可靠的过程相关决策。在第一部分中，我们建立了CMP过程性能之间的定量关系，例如材料去除率（MRR）和从中获取的数据无线振动传感器。随后，将非线性顺序贝叶斯分析与决策理论概念集成在一起，以检测毯式铜晶片的CMP工艺终点。使用这种方法，可以在5％的错误率内检测到CMP抛光终点。接下来，使用非参数贝叶斯分析方法来捕获在CMP中观察到的固有的复杂，非高斯和非平稳传感器信号模式处理。开发了一种进化聚类分析，称为递归嵌套Dirichlet过程（RNDP）方法，用于使用MEMS振动信号监视CMP过程的变化。使用这种新颖的信号分析方法，可以在20毫秒内检测到过程漂移，并且可以将其评估为比传统SPC图表快3-7倍。从应用的角度来看，这对行业非常有益，因为如果可以及时，准确地检测到CMP工艺漂移的发生，将大大减轻晶圆的良率损失。最后，采用非参数贝叶斯建模方法，称为Dirichlet工艺（DP）的产品与多层次的分层信息融合技术相结合，用于监控UPM工艺中的表面光洁度。使用这种方法，基于信息融合理论，可以集成来自六个不同传感器（三个轴的振动和力）的信号模式。观察到，使用实验性UPM传感器数据，基于多传感器信息融合方法的决策比单个传感器的决策精度高15％-30％。这将使在超精密制造应用中能够更准确，更可靠地估算工艺条件。

著录项

  • 作者

    Beyca, Omer Faruk.;

  • 作者单位

    Oklahoma State University.;

  • 授予单位 Oklahoma State University.;
  • 学科 Industrial engineering.;Nanotechnology.;Statistics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 174 p.
  • 总页数 174
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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