Analysis of Correlated Functional Variables for Manufacturing Process Diagnosis

功能过程变量（FPV）在确定各种制造过程的性能方面起着重要作用。FPV的一个例子是在化学机械抛光（CMP）期间晶片上的机械压力的分布。FPV之间的相关性分析将提供，例如，更好地理解CMP工艺中复杂的晶片-抛光垫-抛光液相互作用。这种理解的提高可能会影响20%的晶圆产量，并为单个晶圆工厂带来28亿美元的收入流。因此，该项目的目标是开发一种新的方法来分析相关的FPV，以实现有效的监测和诊断复杂的制造过程。该研究将首先对相关FPV的复杂时间和空间变化进行建模。对于时间变化，每个FPV将被分解为幅度和相位分量，用于区分定时相关和幅度相关。将进行FPV的全局-局部分解，以区分全局和局部变化。对于空间变化，将使用非线性动力学模型来描述FPV之间的时序相关性。将开发一种非线性主成分方法来模拟FPV之间的幅度（幅度/全局/局部）相关性。基于FPV建模，将开发统计程序以检测和诊断相关FPV的变化。通过研究非线性动力学模型中的系数，可以诊断出FPV中时序相关性的变化。用主曲线回归模型对FPV震级相关性的变化进行诊断。该方法将使用USF的CMP测试仪进行验证。与行业合作伙伴的合作将促进半导体制造领域科学和技术发现的广泛传播。它还将帮助广泛的行业实现更好的过程控制和不断减少变化。该项目的成功将通过开发跨学科课程材料、建立CMP测试平台和基于网络的虚拟实验室以及密切的产学合作来促进教学。这提供了机会，通过让研究生/本科生/K-12学生接受跨学科培训和分析微/纳米尺度材料去除过程中FPV的新方法，来培训新的高技能员工队伍，进行过程控制和质量改进。妇女/少数民族学生将通过桥梁博士学位，斯隆奖学金，和REU（本科研究经验）补充招募。

Functional process variables (FPVs) play a significant role in determining the performance of various manufacturing processes. An example of FPVs is the distribution of mechanical pressure on a wafer during chemical-mechanical polishing (CMP). The analysis of the correlation among FPVs will provide, e.g., a better understanding of the complex wafer-pad-slurry interactions in the CMP process. The improved understanding could affect 20% of wafer yield and impact a revenue stream of $2.8 billion for a single wafer fab. Therefore, the objective of the project is to develop a novel methodology for the analysis of correlated FPVs in order to achieve effective monitoring and diagnosis of complex manufacturing processes. The research will first model the complex temporal and spatial variations in correlated FPVs. For temporal variations, each FPV will be decomposed into amplitude and phase components for distinguishing the timing correlation and magnitude correlation. Global-local decomposition of FPVs will be performed to discriminate global and local variations. As to the spatial variations, a nonlinear dynamics model will be used to depict the timing correlation among FPVs. A nonlinear principal component method will be developed to model the magnitude (amplitude/global/local) correlation among FPVs. Based on the FPVs modeling, statistical procedures will be developed to detect and diagnose variations in correlated FPVs. The change of timing correlation in FPVs can be diagnosed through investigating the coefficients in the nonlinear dynamics model. The change of magnitude correlation in FPVs is to be diagnosed using the principal curve regression model. This methodology will be validated using the CMP tester at USF.The collaboration with industry partners will facilitate a broad dissemination of scientific and technological discoveries in semiconductor manufacturing. It will also assist a broad array of industry to achieve better process control and continuous variation reduction. The success of the project will promote teaching and learning through the development of interdisciplinary curricular materials, establishment of a CMP testbed and web-based virtual lab, and a close Industry-University collaboration. This provides opportunities to train a new highly skilled workforce in process control and quality improvement by exposing graduate/undergraduate/K-12 students to interdisciplinary training and novel methods of analyzing FPVs in micro/nano scale material removal processes. Women/minority students will be recruited through Bridges to Doctorate, Sloan fellowships, and REU (Research Experience for Undergraduate) supplements.