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，以有效地捕获化学机械平坦化（CMP）过程中多维过程状态的潜在非线性和非平稳演化，从而实现CMP中图案相关表面形貌的早期缺陷检测，即，凹陷/侵蚀。拟议的研究将：（1）建立将CMP中的工艺异常与从在线传感器信号提取的特征相联系的基本关系，即，- 传感器特征与发展的凹陷和侵蚀之间的映射，从而能够早期检测表面形貌相关的缺陷;以及（2）使用具有非参数特性和数据驱动性质的新型递归嵌套狄利克雷过程（RNDP）模型创建新的在线预测模型，并且可以准确地捕获CMP工艺的非线性/非平稳性并避免可能的模型过拟合和欠拟合。如果成功，这项研究将导致一个技术突破，可以充分利用/整合CMP工艺数据，从而使早期缺陷检测/缓解晶片产量的提高。预期此建议将对促进半导体工业的工艺监测和控制的技术进步产生重大贡献，从而为CMP工艺带来更好的产品（IC）质量和更高的工艺生产率。新的课程，REU，结合本科生和研究生辅导计划，将吸引潜在的学生，特别是来自代表性不足的群体，通过让学生接触基础研究和行业实践，工程相关的研究和教育。研究成果的传播包括专业演讲和出版物、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.