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: 0853983
• 负责人: Jin Wang
• 金额: $ 17万
• 依托单位: Auburn 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=0853983&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; New; Advanced

0853983WangThe 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.

此次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型的无线程高级过程控制（APC）框架。半导体技术是计算机、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推动了半导体行业生产出许多差异化的产品。因此，多产品多工具（“高混合”）制造已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。为了满足高混合生产的新需求，pi计划在运行控制（RtR）、控制绩效评估（CPA）和统计过程监控（SPM）领域进行研究。智力优势：该研究将打破目前螺纹APC的传统，为高混合半导体制造创造一种无螺纹的范式，并为解决高混合生产带来的挑战提供新的理论和技术。通过在不同线程和不同APC组件之间共享信息，将大大提高监控性能，并显著减少所需模型的数量。每个项目的具体优点总结如下。项目一：状态估计与控制模型更新：对非线程状态估计问题进行理论分析；此外，它将开发一种系统的方法，用于非线程状态估计和高混合生产的控制模型更新，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）：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-工厂不匹配和干扰动力学，以同时实现CPA/CPD。此外，它将为高混合晶圆厂的RtR控制器提供第一个非线程CPA/CPD工具。项目3：统计过程监控：计划分析SPM的批量统计模式，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，极大地提高了监视性能，并显著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和APC在整个晶圆厂监控中的关键作用，本研究解决的问题有可能改变工业执行过程控制的方式。由于该框架在开发过程中很少受到限制，因此所提出的框架不仅限于半导体工艺，还可以应用于面向批量的制药、特种化工和聚合物工业，并可以为一般批量过程监控提供新的解决方案和研究方向。本研究促进了半导体制造控制工程师在研究生和本科阶段的教育。目前，美国半导体公司正面临着维持合格的半导体员工队伍的挑战，包括过程控制领域的工程师。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，这些项目是吸引少数民族参与并给予他们半导体过程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士提供有关新过程控制范式的短期课程。