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.

0853748He 这项 GOALI 合作研究的主要目标是开发和验证用于下一代高混合半导体制造的新型非线程高级过程控制 (APC) 框架。半导体技术是计算、通信、消费电子、交通和医疗保健领域革命的核心。近十年来，消费者多样化的需求推动半导体行业生产出许多差异化的产品。结果，多产品多工具（“高混合”）制造日益成为标准制造模式，这带来了当前 APC 框架无法解决的许多挑战。 PI 计划在逐次运行 (RtR) 控制、控制性能评估 (CPA) 和统计过程监控 (SPM) 领域进行研究，以满足高混合生产中的新兴需求。智力优势：该研究将打破当前线程APC的传统，为高混合半导体制造创建非线程范式，并提供新的理论和技术来解决高混合生产带来的挑战。通过在不同线程和不同APC组件之间共享信息，监视和控制性能将大大提高，所需模型的数量将显着减少。每个项目的具体优点总结如下。项目1：状态估计与控制模型更新：提供非线程状态估计问题的理论分析；此外，它将开发一种用于高混合生产的非线程状态估计和控制模型更新的系统方法，该方法通过线性回归公式处理大规模非线性系统。项目 2：控制性能评估和诊断 (CPA/CPD)：所提出的框架不是将实际控制性能与理论基准进行比较，而是明确估计模型-设备失配和扰动动态，以同时实现 CPA/CPD。此外，它将为高混合晶圆厂中的 RtR 控制器提供首款非线程 CPA/CPD 工具。项目3：统计过程监控：计划分析SPM的批次统计模式，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，因为它专门解决了新兴的工业需求。由于半导体工艺的复杂性以及 APC 在整个晶圆厂监控和控制中的关键作用，本研究解决的问题有可能改变行业执行工艺控制的方式。由于框架开发过程中受到的限制很少，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量的制药、特种化学品和聚合物行业，并可以激发一般批量过程监控的新解决方案和研究方向。这项研究促进了研究生和本科生半导体制造控制工程师的教育。目前，美国半导体公司在维持高素质的半导体劳动力（包括过程控制领域的工程师）方面面临着挑战。因此，三所大学都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，这些项目是让少数群体参与并为他们提供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有关新过程控制范例的短期课程。