Semiconductor: COLLABORATIVE RESEARCH: Hierarchical Modeling of Yield and Defectivity to Improve Factory Operations

该资助为开发模型提供资金，以了解子过程输出对产品性能特征的影响和贡献，例如产量，符合规格，缺陷，客户退货或半导体制造环境中的其他质量指标。 制造过程和制造后评估之间的建模关系是很重要的。 然而，像这样的高维复杂系统在数据分析中提出了许多独特的挑战。 将开发方法论的方法来解决在半导体制造中出现的复杂建模问题，利用广义线性模型（GLM）的层次结构。 这将通过首先开发集成数据结构的基础来实现，这些数据结构随后将用于构建和评估分层模型。 本研究亦将平面线性模型的理论延伸至阶层式结构，若能成功，本研究的结果将可改善操作模型及阶层式模型。 在操作层面上的关系的特点将提高生产力，确定和验证质量控制参数，并使竞争过程模型的评估。 本研究的结果将影响制造业务，因为它将提高技术资源的利用率，并提供了检查当前操作方法和指标的有效性和有效性的手段。 方法上的进步将在几个领域：先进的技术水平，使用数据来估计高维系统的分层建模;探索数据操作策略和大型复杂的制造系统建模之间的相互作用;和开发高维建模算法结合非线性统计建模的进步。

This grant provides funding for developing models to understand the influences and contributions of sub-process outputs on product performance characteristics, such as yield, conformance to specifications, defectivity, customer returns, or other quality measures in a semiconductor manufacturing environment. Modeling relationships between fabrication processes and post-fabrication assessment is important. However, high dimensional complex systems, such as this, present a number of unique challenges in data analysis for understanding. Methodological approaches will be developed to solve the complex modeling problems that arise in semiconductor manufacturing by utilizing Generalized Linear Models (GLMs) in a hierarchical structure. This will be accomplished by first developing the foundation for integrating data structures that will be subsequently used in building and evaluating hierarchical models. This research will also extend the theory of flat, linear models into a hierarchical structure.If successful, the results of this research will lead to improvements in operational modeling and hierarchical models. Characterizing the relationships at the operational level will improve productivity, identify and validate quality control parameters, and enable evaluation of competing process models. The results of this research will impact manufacturing operations in that it will improve the utilization of technical resources and provide the means to examine the validity and effectiveness of current operating methods and metrics. Methodological advances will be made in several areas: advance the state of the art in using data to estimate high-dimensional systems by means of hierarchical modeling; exploring the interaction between data manipulation strategies and modeling large and complex manufacturing systems; and developing high dimensional modeling algorithms in combination with advances in nonlinear statistical modeling.