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: 0853748
• 负责人: QINGHUA HE
• 金额: $ 9.8万
• 依托单位: Tuskegee University
• 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 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0853748&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; New; Advanced

0853748He 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.

0853748他的合作目标研究的主要目标是开发和验证新型的非线程高级过程控制（APC）框架，用于下一代高混合半导体制造。半导体技术是计算，通信，消费电子，运输和医疗保健的革命核心。在过去的十年中，消费者的多样化需求一直在推动半导体行业生产许多差异化产品。结果，多产品 - 媒体工具（“高混合”）制造已经越来越成为标准制造模型，这构成了当前APC框架无法解决的许多挑战。 PIS计划在运行（RTR）控制，控制绩效评估（CPA）和统计过程监测（SPM）领域的PIS计划研究，以满足高混合生产的新兴需求。知识分子的优点：这项研究将通过脱离当前的螺纹APC传统，为高混合半导体制造创建一个非线程范式，并提供新的理论和技术来应对高混合产量所带来的挑战。通过在不同线程和不同的APC组件之间共享信息，将大大改善监视和控制性能，并大大减少所需模型的数量。每个项目的具体优点在下面总结。项目1：状态估计和控制模型更新：它将提供有关非线程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系统的方法，该方法通过线性回归公式来处理大规模的非线性系统。项目2：控制绩效评估和诊断（CPA/CPD）：拟议的框架明确估计模型 - 植物不匹配和干扰动力学以同时实现CPA/CPD，而不是将实际控制绩效与理论基准进行比较。此外，它将为RTR控制器提供第一个非线程CPA/CPD工具。项目3：统计过程监视：分析批处理统计的模式，而不是SPM的过程变量模式。该方法消除了螺纹方法所需的数据预处理，从而大大提高了监视性能，并大大减少了所需模型的数量。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，因为它专门满足了新兴的工业需求。由于半导体过程的复杂性以及APC在FAB范围内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行业执行过程控制的方式。由于在框架开发过程中很少施加限制，因此所提出的框架不限于半导体过程，因此，它也可以应用于面向批处理的药物，特种化学和聚合物工业，并且可以激发一般批次过程监测和控制中的新解决方案和研究指导。这项研究促进了在研究生和本科级别的半导体制造的控制工程师的教育。目前，美国半导体公司在维持合格的半导体劳动力方面面临挑战，包括过程控制领域的工程师。因此，这三所大学致力于对半导体制造过程控制中的学生和专业人员的继续教育和培训。此外，这些项目是参与少数群体并为他们提供半导体过程控制方面的研究经验的潜在资源。最后，PI将向半导体行业的中级专业人员提供有关新工艺​​控制范式的简短课程。