A Recurrent Nested Bayesian Non-parametric Model for Real Time Monitoring of Pattern Dependent Surface Topography in Chemical Mechanical Planarization (CMP) Operations

该奖项的研究目标是开发一种新的方法，有效地捕捉化学机械平坦化(CMP)过程中潜在的多维过程状态的非线性和非平稳演变，以便能够及早检测化学机械平坦化过程中依赖于图案的表面形貌的缺陷，即碟状/侵蚀。这项研究将：(1)建立化学机械抛光过程异常与从在线传感器信号中提取的特征之间的基本关系，即传感器特征与不断演变的碟状和侵蚀之间的映射关系，从而能够及早检测与表面形貌相关的缺陷；(2)利用一种新的递归嵌套Dirichlet过程(RNDP)模型建立新的在线预测模型，该模型具有非参数性质和数据驱动性质，能够准确地捕捉化学机械抛光过程的非线性/非平稳性，避免可能的模型过度和欠拟合，如果研究成功，将带来技术突破，能够充分利用/集成化学机械抛光过程数据，从而实现早期缺陷检测/缓解，从而提高晶圆成品率。预计这项提议将对促进半导体行业工艺监测和控制的技术进步做出重大贡献，从而为化学机械抛光工艺带来更好的产品(IC)质量和更高的工艺生产率。新课程REU与本科生和研究生指导计划相结合，将通过让学生接触基础研究和行业实践，吸引潜在学生，特别是来自代表性不足群体的学生，从事与工程相关的研究和教育。研究成果的传播包括专业介绍和出版物、网站开发、媒体宣传和学生出版物，以及与行业合作伙伴的合作。

The research objective of this award is to develop a new approach to effectively capture the underlying nonlinear and nonstationary evolution of the multi-dimensional process states in Chemical Mechanical Planarization (CMP) process, to enable early defect detection of pattern dependent surface topography in CMP, i.e., dishing/erosion. The proposed research will: (1) establish the fundamental relationships that connect process abnormalities in CMP with extracted features from online sensor signals, i.e., a mapping between the sensor features with the evolving dishing and erosion, thus, to enable early detection of surface topography related defects; and (2) create a new online predictive model with a novel recurrent nested Dirichlet process (RNDP) model which has a non-parametric property and data-driven nature, and can accurately capture CMP process nonlinearity/nonstationarity and avoid the possible model over- and under-fitting.If successful, this research will result in a technological breakthrough that can fully utilize/integrate the CMP process data and thus enable early defect detection/alleviation for wafer yield improvement. It is anticipated that this proposal would generate significant contributions toward promoting the technological advances in process monitoring and control for semiconductor industry, leading to a better product (IC) quality and higher process productivity for the CMP process. The new curricula, REU, combined with undergraduate and graduate student mentoring programs, will attract potential students, especially from underrepresented groups, to engineering related research and education by exposing students to both fundamental research and industry practices. Dissemination of research outcomes includes professional presentations and publications, website development, media outreach and student publications, as well as collaboration with industry partners.