Collaborative Research: GOALI: A New Advanced Process Control Framework for Next-Generation High-Mix Semiconductor Manufacturing

合作研究：GOALI：用于下一代高混合半导体制造的新型先进过程控制框架

• 批准号: 0854033
• 负责人: Thomas Edgar
• 金额: $ 17.35万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0854033&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; New; Advanced

0854033Edgar The primary goal of this collaborative GOALI research is to develop and validate a novel non-threaded advanced process control (APC) framework for next-generation high-mix semiconductor manufacturing. Semiconductor technology lies at the heart of the revolution in computing, communications, consumer electronics, transportation and health care. In the last decade, diversified demand from consumers has been pushing semiconductor industry to produce many differentiated products. As a result, multi-product-multi-tool ("high-mix") manufacturing has become increasingly the standard manufacturing model, which poses many challenges that the current APC framework cannot address. The PIs plan research in the fields of run-to-run (RtR) control, control performance assessment (CPA) and statistical process monitoring (SPM) to meet the emerging needs in high-mix production. Intellectual Merit: The research will create a non-threaded paradigm for high-mix semiconductor manufacturing by breaking from the current tradition of threaded APC, and provide new theories and techniques to address the challenges posed by high-mix production. By sharing information among different threads and different APC components, monitoring and control performance will be greatly improved and the number of required models will be significantly reduced. Specifically merits of each project are summarized below. Project 1: State estimation and control model update: It will provide theoretical analysis on the non-threaded state estimation problem; in addition, it will develop a systematic approach for non-threaded state estimation and control model update for high-mix production, which handles large-scale nonlinear systems through a linear regression formulation. Project 2: Control performance assessment and diagnosis (CPA/CPD): Instead of comparing the actual control performance against a theoretical benchmark, the proposed framework explicitly estimates model-plant mismatch and disturbance dynamics to achieve CPA/CPD simultaneously. In addition, it will provide the first non-threaded CPA/CPD tools for RtR controllers in high-mix fabs. Project 3: Statistical process monitoring: Analyzing the pattern of batch statistics instead of the pattern of process variables for SPM is planned. The approach eliminates data pre-processing required by threaded methods, greatly improves monitoring performance, and significantly reduces the number of required models. Broader Impact: This research will have an immediate impact on the industrial practice of semiconductor manufacturing, as it specifically addresses emerging industrial needs. Due to the complexity of semiconductor processes and the critical role of APC in fab-wide monitoring and control, the problem addressed in this research has the potential to transform the way industry performs process control. Because few restrictions were posed during the framework development, the proposed framework is not limited to the semiconductor processes, instead, it can also be applied to the batch-oriented pharmaceutical, specialty chemical, and polymer industries and could inspire new solutions and research directions in general batch process monitoring and control. This research promotes the education of control engineers for semiconductor manufacturing at both graduate and undergraduate levels. Currently, U.S. semiconductor companies are facing challenges in sustaining a well-qualified semiconductor workforce, including engineers in the area of process control. Therefore, the three universities are committed to the continuing education and training of students and professionals in semiconductor manufacturing process control. Moreover, these projects are potential resources for involving minorities and giving them research experience in semiconductor process control. Finally, the PIs will offer short courses on the new process control paradigm to mid-career professionals in the semiconductor industries.

0854033 Edgar GOALI合作研究的主要目标是为下一代高混合半导体制造开发和验证一种新的非线程先进工艺控制（APC）框架。半导体技术是计算、通信、消费电子、交通和医疗保健革命的核心。在过去的十年中，消费者的多样化需求一直推动半导体行业生产许多差异化产品。因此，多产品多工具（“高混合”）制造已日益成为标准的制造模式，这带来了许多挑战，目前的先进工艺控制框架无法解决。PI计划在运行到运行（RtR）控制，控制性能评估（CPA）和统计过程监控（SPM）领域进行研究，以满足高混合生产的新需求。智力优势：该研究将通过打破当前线程APC的传统，为高混合半导体制造创建一个非线程范例，并提供新的理论和技术来应对高混合生产带来的挑战。通过在不同的线程和不同的APC组件之间共享信息，监视和控制性能将大大提高，所需的模型数量将大大减少。每个项目的具体优点总结如下。项目一：状态估计和控制模型更新：它将提供非线程状态估计问题的理论分析;此外，它将为高混合生产的非线程状态估计和控制模型更新开发一种系统方法，通过线性回归公式处理大规模非线性系统。项目二：控制性能评估和诊断（CPA/CPD）：而不是比较实际的控制性能与理论基准，所提出的框架明确估计模型-植物失配和干扰动态，同时实现CPA/CPD。此外，它还将为高混合晶圆厂的RtR控制器提供首个非线程CPA/CPD工具。项目3：统计过程监测：计划分析SPM的批统计模式，而不是过程变量模式。该方法消除了线程方法所需的数据预处理，大大提高了监控性能，并显着减少了所需的模型数量。更广泛的影响：这项研究将对半导体制造的工业实践产生直接影响，因为它专门解决了新兴的工业需求。由于半导体工艺的复杂性和APC在整个工厂的监测和控制中的关键作用，本研究中解决的问题有可能改变工业过程控制的方式。由于在框架开发过程中提出的限制很少，因此所提出的框架不仅限于半导体工艺，相反，它还可以应用于面向批处理的制药，特种化学品和聚合物行业，并可以激发一般批处理过程监测和控制的新解决方案和研究方向。这项研究促进了半导体制造业控制工程师在研究生和本科阶段的教育。目前，美国半导体公司在维持高素质的半导体劳动力方面面临挑战，包括工艺控制领域的工程师。因此，这三所大学致力于半导体制造过程控制方面的学生和专业人员的继续教育和培训。此外，这些项目是使少数民族参与并使他们获得半导体工艺控制研究经验的潜在资源。最后，PI将为半导体行业的职业中期专业人员提供关于新过程控制模式的短期课程。