research-article

Skipping algorithms for defect inspection using a dynamic control strategy in semiconductor manufacturing

Authors:

Gloria Luz Rodriguez-Verjan,

Stéphane Dauzère-Pérès,

Sylvain Housseman,

Jacques PinatonAuthors Info & Claims

WSC '13: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World

Pages 3684 - 3695

Published: 08 December 2013 Publication History

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Abstract

In this paper, we propose new ways for efficiently managing defect inspection queues in semiconductor manufacturing when a dynamic sampling strategy is used. The objective is to identify lots that can skip the inspection operation, i.e. lots that have limited impact on the risk level of process tools. The risk considered in this paper, called Wafer at Risk (W@R), is the number of wafers processed on a process tool between two defect inspection operations. An indicator (GSI, Global Sampling Indicator) is used to evaluate the overall W@R and another associated indicator (LSI, Lot Scheduling Indicator) is used to identify the impact on the overall risk if a lot is not measured. Based on these indicators, five new algorithms are proposed and tested with industrial instances. Results show the relevance of our approach and that evaluating sets of lots for skipping performs better than evaluating lots individually.

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Cited By

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Quéré ÉDauzère-Pérès STamssaouet KMaufront CAstie S(2020)Dynamic sampling for risk minimization in semiconductor manufacturingProceedings of the Winter Simulation Conference10.5555/3466184.3466397(1886-1897)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466397
Dauzère-Pérès SHassoun MSendon AHuschka TChick S(2016)Optimizing capacity assignment of multiple identical metrology toolsProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042432(2709-2718)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042432
Sendón ADauzère-Pérès SPinaton J(2015)Simulation model to control risk levels on process equipment through metrology in semiconductor manufacturingProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888957(2941-2952)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888957

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Information

Published In

WSC '13: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World

December 2013

4386 pages

ISBN:9781479920778

General Chair:
Raymond R. Hill
Air Force Institute of Technology
,
Program Chair:
Michael E. Kuhl
Rochester Institute of Technology

Publisher

IEEE Press

Publication History

Published: 08 December 2013

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WSC '13

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IIE
INFORMS-SIM
ASA
SIGSIM
SCS
ASIM
IEEE/SMCS
NIST

WSC '13: Winter Simulation Conference

December 8 - 11, 2013

D.C., Washington

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Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Quéré ÉDauzère-Pérès STamssaouet KMaufront CAstie S(2020)Dynamic sampling for risk minimization in semiconductor manufacturingProceedings of the Winter Simulation Conference10.5555/3466184.3466397(1886-1897)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466397
Dauzère-Pérès SHassoun MSendon AHuschka TChick S(2016)Optimizing capacity assignment of multiple identical metrology toolsProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042432(2709-2718)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042432
Sendón ADauzère-Pérès SPinaton J(2015)Simulation model to control risk levels on process equipment through metrology in semiconductor manufacturingProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888957(2941-2952)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888957

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Optimized allocation of defect inspection capacity with a dynamic sampling strategy

A mathematical model for estimating defect inspection capacity with a dynamic control strategy

Improving defect detection ability with adaptive software inspection checklists