GOALI: Adjustment and Monitoring Methods for Multiple-Stream and Process-Oriented Quality Control

GOALI：多流和面向过程的质量控制的调整和监控方法

• 批准号: 0085041
• 负责人: George Runger
• 金额: $ 20万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0085041&HistoricalAwards=false
• 关键词: GOALI; Adjustment; Monitoring; Methods; Multiple

Multivariate statistical process control research has produced tools that can be used to identify when irregularities in production occur and to characterize the components of this variation. The diagnosis and control actions, in the sense of process adjustment, are not modeled and it is up to the process engineer to interpret and correct causes of variation. The presence of quality characteristics that drift with time (auto-correlation) and that vary in similar ways across several characteristics (cross-correlation) makes multivariate statistical process control a difficult task. For these reasons, interest exists on integrating process adjustment techniques with statistical process monitoring tools. The major components of variation in quality data can be found by decomposing the data according to principal component analysis, but this is a data-oriented approach and not based on any process knowledge, which makes interpretation difficult. The process-oriented basis representation (POBREP) analysis uses process knowledge to decompose quality data into cause-associated components. In POBREP, each potential production problem is associated with one basis element. This research investigates the thesis that POBREP can provide an effective tool for process adjustment. It has been shown previously how POBREP can be used for process monitoring purposes. For process adjustment, the following questions, among others, will be investigated: (1) What are the appropriate statistical models for adjustment that incorporate POBREP knowledge? (2) When is POBREP likely to provide a performance advantage? and (3) Can POBREP be applied effectively to a wafer fabrication process? There are several benefits associated with this research. A monitoring and adjustment strategy based on anticipated problems and disturbances can transform the ineffective performance of an omnibus control strategy. The work includes collaboration between researchers at Arizona State, Intel, and Penn State. The collaboration includes eight-month internships at Intel for Penn State and Arizona State graduate research assistants, visits to Intel by Penn State faculty, and regular visits to Intel by Arizona State faculty. There are extensive infrastructure benefits related to this collaborative approach: (1) synergistic benefits of coordinated research from four previously separately sponsored NSF researchers; (2) GOALI benefits, including engineers in the classroom, faculty visits to industry, etc.; (3) the opportunity to leverage results using existing laboratory equipment; and (4) to enhance existing courses in Applied Statistics at Penn State and Arizona State.

多变量统计过程控制研究已经产生了一些工具，可以用来识别生产中何时出现不规则现象，并描述这种变化的组成部分。 在过程调整的意义上，诊断和控制动作没有被建模，并且由过程工程师来解释和纠正变化的原因。 质量特性随时间漂移（自相关）和以类似方式在几个特性之间变化（互相关）的存在使得多变量统计过程控制成为一项困难的任务。由于这些原因，存在的兴趣与统计过程监控工具集成的过程调整技术。 通过主成分分析法对数据进行分解，可以找出质量数据变化的主要成分，但这是一种面向数据的方法，不基于任何工艺知识，因此难以进行解释。面向过程的基础表示（POBREP）分析使用过程知识将质量数据分解为与原因相关的组件。 在POBREP中，每个潜在的生产问题都与一个基本要素相关联。本研究探讨了POBREP可以提供一个有效的过程调整工具的论点。 前面已经介绍了POBREP如何用于过程监控。 对于流程调整，将调查以下问题：（1）纳入POBREP知识的调整的适当统计模型是什么？ (2)POBREP何时可能提供性能优势？以及（3）POBREP能否有效地应用于晶片制造工艺？这项研究有几个好处。 基于预期问题和干扰的监控和调整策略可以改变综合控制策略的无效性能。 这项工作包括亚利桑那州立大学、英特尔和宾夕法尼亚州立大学的研究人员之间的合作。 此次合作包括在英特尔为宾夕法尼亚州立大学和亚利桑那州立大学的研究生研究助理提供为期八个月的实习机会，宾夕法尼亚州立大学的教职员工访问英特尔，亚利桑那州立大学的教职员工定期访问英特尔。这种合作方法具有广泛的基础设施优势：（1）来自先前分别资助的四位NSF研究人员的协调研究的协同效益;（2）GOALI的好处，包括工程师在课堂上，教师访问工业等; (3)利用现有实验室设备利用结果的机会;（4）加强宾夕法尼亚州立大学和亚利桑那州立大学应用统计学的现有课程。