GOALI: Processing, System Modeling and Process Control for Complicated Functional Data

目标：复杂功能数据的处理、系统建模和过程控制

• 批准号: 0400071
• 负责人: Jye-Chyi Lu
• 金额: $ 23.51万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0400071&HistoricalAwards=false
• 关键词: GOALI; Processing; System; Modeling; Process

This Grant Opportunity for Academic Liaison with Industry (GOALI) award provides funding for the development of methodology and algorithm for processing large-size functional data with sharp changes and nonstationary patterns from manufacturing systems. The developed methods will integrate wavelet-based signal-processing techniques and neural-networks oriented system-modeling procedures to develop an in-situ process control tool for manufacturing processes such as ultra-thin semiconductor-film deposition. The research will optimize a generic data-reduction objective function balancing data-compression goals and modeling accuracy requirements for various types of data analysis and decision-making use. The developed statistical process control algorithm aiming to study process deviates at selective wavelet parameters will be more effective in detecting local changes in monitoring complicated functional data. Experimenting the developed procedures on various testing data curves obtained from the literature by simulating different amount of data noises and signal-change patterns will provide insights of the strength and weakness of the proposed methods against extensions of existing work for data-reduction purposes. Our industry partner from PDF Solutions, Inc. will work with us in providing insights for tuning the researched methods in solving real-life problems and in testing the developed methods and tools in practical applications.If successful, the results of this research will lead to improvement in algorithms for in-situ process control and a locally focused neural network, and new development in analysis and modeling of large-size complicated functional data. The primary goal of this work is to provide a generic data-reduction tool with a wide range of applications in mining knowledge from massive and complex functional data. Getting the relevant knowledge will help practitioners identify process problems more efficiently and improve the quality of manufacturing systems more effectively. The experience learned in this interdisciplinary project will nourish our integrated education and research program for training the next generation of manufacturing enterprise system engineers.

该“学术与工业联络机会”(GOALI) 奖项为开发方法和算法提供资金，用于处理制造系统中具有急剧变化和非平稳模式的大型功能数据。 所开发的方法将集成基于小波的信号处理技术和面向神经网络的系统建模程序，以开发用于超薄半导体薄膜沉积等制造工艺的原位工艺控制工具。 该研究将优化通用数据缩减目标函数，平衡数据压缩目标和各种类型数据分析和决策使用的建模精度要求。 所开发的统计过程控制算法旨在研究选择性小波参数下的过程偏差，将更有效地检测监控复杂功能数据的局部变化。 通过模拟不同数量的数据噪声和信号变化模式，在从文献中获得的各种测试数据曲线上对开发的程序进行实验，将提供所提出的方法的优缺点以及针对数据缩减目的现有工作的扩展的见解。 我们来自 PDF Solutions, Inc. 的行业合作伙伴将与我们合作，为调整解决现实问题的研究方法以及在实际应用中测试开发的方法和工具提供见解。如果成功，这项研究的结果将导致原位过程控制和局部聚焦神经网络算法的改进，以及大型复杂功能数据分析和建模的新发展。 这项工作的主要目标是提供一种通用的数据缩减工具，在从海量复杂的功能数据中挖掘知识方面具有广泛的应用。 获得相关知识将有助于从业人员更有效地识别工艺问题，更有效地提高制造系统的质量。 在这个跨学科项目中学到的经验将滋养我们的综合教育和研究计划，以培训下一代制造企业系统工程师。