Process performance prediction for chemical mechanical planarization (CMP) by integration of nonlinear bayesian analysis and statistical modeling

Zhenyu Kong*, Asil Oztekin, Omer Faruk Beyca, Upendra Phatak, Satish T.S. Bukkapatnam, Ranga Komanduri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

Chemical mechanical planarization (CMP) process has been widely used in the semiconductor manufacturing industry for realizing highly finished (Ra ∼1 nm) and planar surfaces (WIWNU ∼ 1%, thickness standard deviation (SD) ∼ nm) of in-process wafer polishing. The CMP process is rather complex with nonlinear and non-Gaussian process dynamics, which brings significant challenges for process monitoring and control. As an attempt to address this issue, a method is presented in this paper that integrates nonlinear Bayesian analysis and statistical modeling to estimate and predict process state variables, and therewith to predict the performance measures, such as material removal rate (MRR), surface finish, surface defects, etc. As an example of performance measure, MRR is chosen to demonstrate the performance prediction. A sequential Monte Carlo (SMC) method, namely, particle filtering (PF) method is utilized for nonlinear Bayesian analysis to predict the CMP process-state and for tackling the process nonlinearity. Vibration signals from both wired and wireless vibration sensors are adopted in the experimental study conducted using the CMP apparatus. The process states captured by the sensor signals are related to MRR using design of experiments and statistical regression analysis. A case study was conducted using actual CMP processing data by comparing the PF method with other widely used prediction approaches. This comparison demonstrates the effectiveness of the proposed approach, especially for nonlinear dynamic processes.

Original languageEnglish
Article number5433049
Pages (from-to)316-327
Number of pages12
JournalIEEE Transactions on Semiconductor Manufacturing
Volume23
Issue number2
DOIs
Publication statusPublished - May 2010
Externally publishedYes

Funding

The authors would like to thank the National Science Foundation, Division of CMMI for its kind support of this work. R. Komanduri wishes to thank the A. H. Nelson, Jr. Chair for the additional financial support. Thanks are also due to Mrs. S. Green for excellent editorial support. Manuscript received June 26, 2008; revised October 23, 2009. First version published March 18, 2010; current version published May 05, 2010. This work was supported by the National Science Foundation under Grant CMMI-0700680 and Grant CMMI-0830023. The work of R. Komanduri was supported in part by the A. H. Nelson Jr. Endowed Chair.

FundersFunder number
Division of CMMI
National Science FoundationCMMI-0830023, CMMI-0700680

    Keywords

    • Bayesian analysis
    • Chemical mechanical planarization (CMP)
    • Design of experiments
    • Particle filtering
    • Process performance prediction
    • Vibration sensors

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