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.
