MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

P. K S Prakash*, Andrea Schirru, Peter Hung, Seán McLoone

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models.

Original languageEnglish
Title of host publicationASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
Pages45-50
Number of pages6
DOIs
Publication statusPublished - 23 Jul 2012
Event2012 23rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2012 - NY, Saratoga Springs, United States
Duration: 15 May 201217 May 2012

Conference

Conference2012 23rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2012
CountryUnited States
CitySaratoga Springs
Period15/05/201217/05/2012

Keywords

  • clustering
  • optical emission spectroscopy
  • plasma etch processes
  • regression
  • Virtual metrology

ASJC Scopus subject areas

  • Engineering(all)

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    Prakash, P. K. S., Schirru, A., Hung, P., & McLoone, S. (2012). MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces. In ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings (pp. 45-50). [6212866] https://doi.org/10.1109/ASMC.2012.6212866