Statistical Methods for Process Control and Improvement in Advanced Manufacturing

先进制造过程控制和改进的统计方法

• 批准号: 9803281
• 负责人: Vijayan Nair
• 金额: $ 20.9万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9803281&HistoricalAwards=false
• 关键词: Statistical; Methods; Process; Control; Improvement

Proposal Number: DMS 9803281 PI: Vijayan N. Nair, University of Michigan Mark H. Hansen, Bell Labs, Lucent Technologies Project: Statistical Methods for Process Control and Improvement in Advanced Manufacturing Abstract: This research project deals with methods for modeling, monitoring, diagnosis, and improvement of manufacturing processes with spatial data. The scope of the project goes beyond the traditional Shewhart's paradigm for statistical process control (SPC) which focuses primarily on process monitoring. A significant part of the research is the use of in-process and product quality data to develop failure diagnostics and to relate these to potential problems for process improvement. These issues are studied in the context of an integrated framework for process control and improvement. An overall strategy is proposed for using the spatial information in defect clustering as the basis for process improvement. The methodology consists of several parts. First, process monitoring methods for routinely monitoring the spatial data and detecting objects with significant clustering are developed. A Markov random field model with small-scale clustering is used to characterize ``in-control'' data. Statistical methods for failure diagnosis (signatures of spatial patterns) are obtained and the patterns are then related to process information for improvement. Various approaches for doing this including parametric models for large-scale clustering and formal methods based on classification are studied. Several other related topics, including modeling and analysis of ordinal data from temporal and spatial processes and the analysis of spatial data from designed experiments are also studied. These methods are developed and studied in the specific context of wafer map data in integrated circuit (IC) fabrication. Semiconductor manufacturing is one of the key manufacturing industries in the US, and hence statistical methods for process and yield improvement are clearly important from a practical viewpoint. However, the research issues and methods developed here are quite generic in nature and are of general interest to many other manufacturing processes with spatial data, including flat panel displays, printed circuit boards, and the manufacture and assembly of auto-bodies. These advanced manufacturing and high-technology industries all share the following features. Massive amounts of in-process and production data are now being collected routinely, made possible by advances in computing and data capture technologies. Much of these data have complex structures, in the form of spatial objects, images and so on. At the same time, competitive market pressures are placing a lot of emphasis on reducing product development cycle time. Moreover, process/product designers are operating on the boundaries of available subject matter knowledge of the underlying technology. Products are being manufactured and marketed before the technology is well-understood. These manufacturing processes are often not ``stable'', as is commonly assumed in the traditional statistical process control (SPC) paradigm. Thus, there is a critical need for statistical methods that exploit the extensive information available from in-process and product quality data not only for process monitoring but also for process improvement. The results from this research project will significantly advance methodology for the continuous improvement of advanced manufacturing processes.

提案编号：DMS 9803281 PI：Vijayan N.密歇根大学 Mark H.汉森、贝尔实验室、朗讯科技 项目：过程控制和改进的统计方法 在先进制造 摘要： 该研究项目涉及建模，监测， 诊断和改进制造过程， 数据 该项目的范围超出了传统的休哈特的统计过程控制（SPC），主要集中在过程监测的范式。 研究的一个重要部分是使用过程和产品质量数据来开发故障 诊断，并将其与潜在问题联系起来，以进行工艺改进。这些问题的背景下进行研究的过程控制和改进的综合框架。提出了利用缺陷聚类中的空间信息作为工艺改进依据的总体策略。 该方法包括几个 零件.首先，开发了用于常规监测空间数据和检测具有显著聚类的对象的过程监测方法。一个马尔可夫随机场模型与小规模聚类是用来表征“在控制”的数据。故障诊断的统计方法（签名的空间模式），然后模式相关的过程信息进行改进。 实现这一目标的各种方法，包括参数模型 研究了大规模聚类和基于分类的形式化方法。其他几个相关的主题，包括建模和分析的顺序数据的时间和空间过程和空间数据的分析设计的实验也进行了研究。这些方法是在集成电路（IC）制造中的晶圆图数据的特定背景下开发和研究的。 半导体制造是制造业的关键之一 美国的工业，因此统计方法的过程和 从实际观点来看，产量提高显然是重要的。然而，这里所发展的研究问题和方法在本质上是相当通用的，具有普遍性。 对于许多其他具有空间数据制造过程， 包括平板显示器、印刷电路板， 汽车车身的制造和装配。这些先进制造业和高科技产业都有以下特点。 大量的过程中和生产数据现在正在 由于计算机和数据采集技术的进步，这些数据得以定期收集。这些数据中的大部分具有复杂的结构， 以空间物体、图像等形式呈现。与此同时，竞争激烈的市场压力越来越强调缩短产品开发周期。此外，工艺/产品设计人员在基础技术的可用主题知识的边界上操作。 在技术被充分理解之前，产品就已经被制造和销售了。这些制造过程往往不是“稳定的”，因为通常假设在传统的统计过程控制（SPC）的范例。因此，有一个关键的需要，统计方法，利用广泛的信息，从过程中和产品质量数据，不仅为过程监控，而且为过程改进。该研究项目的结果将大大推进先进制造工艺持续改进的方法。