Model context selection for run-to-run control

O. Arda Vanli, Nital S. Patel, Mani Janakiram, Enrique Del Castillo

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In the design of run-to-run controllers one is usually faced with the problem of selecting a model structure that best explains the variability in the data. The variable selection problem often becomes more complex when there are large numbers of candidate variables and the usual regression modeling assumptions are not satisfied. This paper proposes a model selection approach that uses ideas from the statistical linear models and stepwise regression literature to identify the context variables that contribute most to the autocorrelation and to the offsets in the data. A simulation example and an application to lithography alignment control are presented to illustrate the approach.

Original languageEnglish (US)
Article number4369349
Pages (from-to)506-516
Number of pages11
JournalIEEE Transactions on Semiconductor Manufacturing
Volume20
Issue number4
DOIs
StatePublished - Nov 1 2007

Fingerprint

regression analysis
Model structures
Autocorrelation
Lithography
Controllers
autocorrelation
controllers
lithography
alignment
simulation

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Vanli, O. Arda ; Patel, Nital S. ; Janakiram, Mani ; Del Castillo, Enrique. / Model context selection for run-to-run control. In: IEEE Transactions on Semiconductor Manufacturing. 2007 ; Vol. 20, No. 4. pp. 506-516.
@article{e6b45408c6de44aa80c4664d2dc65ed8,
title = "Model context selection for run-to-run control",
abstract = "In the design of run-to-run controllers one is usually faced with the problem of selecting a model structure that best explains the variability in the data. The variable selection problem often becomes more complex when there are large numbers of candidate variables and the usual regression modeling assumptions are not satisfied. This paper proposes a model selection approach that uses ideas from the statistical linear models and stepwise regression literature to identify the context variables that contribute most to the autocorrelation and to the offsets in the data. A simulation example and an application to lithography alignment control are presented to illustrate the approach.",
author = "Vanli, {O. Arda} and Patel, {Nital S.} and Mani Janakiram and {Del Castillo}, Enrique",
year = "2007",
month = "11",
day = "1",
doi = "10.1109/TSM.2007.907628",
language = "English (US)",
volume = "20",
pages = "506--516",
journal = "IEEE Transactions on Semiconductor Manufacturing",
issn = "0894-6507",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

Model context selection for run-to-run control. / Vanli, O. Arda; Patel, Nital S.; Janakiram, Mani; Del Castillo, Enrique.

In: IEEE Transactions on Semiconductor Manufacturing, Vol. 20, No. 4, 4369349, 01.11.2007, p. 506-516.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Model context selection for run-to-run control

AU - Vanli, O. Arda

AU - Patel, Nital S.

AU - Janakiram, Mani

AU - Del Castillo, Enrique

PY - 2007/11/1

Y1 - 2007/11/1

N2 - In the design of run-to-run controllers one is usually faced with the problem of selecting a model structure that best explains the variability in the data. The variable selection problem often becomes more complex when there are large numbers of candidate variables and the usual regression modeling assumptions are not satisfied. This paper proposes a model selection approach that uses ideas from the statistical linear models and stepwise regression literature to identify the context variables that contribute most to the autocorrelation and to the offsets in the data. A simulation example and an application to lithography alignment control are presented to illustrate the approach.

AB - In the design of run-to-run controllers one is usually faced with the problem of selecting a model structure that best explains the variability in the data. The variable selection problem often becomes more complex when there are large numbers of candidate variables and the usual regression modeling assumptions are not satisfied. This paper proposes a model selection approach that uses ideas from the statistical linear models and stepwise regression literature to identify the context variables that contribute most to the autocorrelation and to the offsets in the data. A simulation example and an application to lithography alignment control are presented to illustrate the approach.

UR - http://www.scopus.com/inward/record.url?scp=63249120290&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=63249120290&partnerID=8YFLogxK

U2 - 10.1109/TSM.2007.907628

DO - 10.1109/TSM.2007.907628

M3 - Article

AN - SCOPUS:63249120290

VL - 20

SP - 506

EP - 516

JO - IEEE Transactions on Semiconductor Manufacturing

JF - IEEE Transactions on Semiconductor Manufacturing

SN - 0894-6507

IS - 4

M1 - 4369349

ER -