Identifying key process characteristics and predicting etch rate from High-Dimension Datasets

E. Ragnoli; S. McLoone; S. Lynn; J. Ringwood; N. Macgearailt

doi:10.1109/ASMC.2009.5155966

Identifying key process characteristics and predicting etch rate from High-Dimension Datasets

E. Ragnoli^*, S. McLoone, S. Lynn, J. Ringwood, N. Macgearailt

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Citations (Scopus)

Abstract

In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.

Original language	English
Title of host publication	2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009
Pages	106-111
Number of pages	6
DOIs	https://doi.org/10.1109/ASMC.2009.5155966
Publication status	Published - 27 Oct 2009
Externally published	Yes
Event	2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009 - Berlin, Germany Duration: 10 May 2009 → 12 May 2009

Conference

Conference	2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009
Country/Territory	Germany
City	Berlin
Period	10/05/2009 → 12/05/2009

ASJC Scopus subject areas

General Engineering

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10.1109/ASMC.2009.5155966

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title = "Identifying key process characteristics and predicting etch rate from High-Dimension Datasets",

abstract = "In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.",

author = "E. Ragnoli and S. McLoone and S. Lynn and J. Ringwood and N. Macgearailt",

year = "2009",

month = oct,

day = "27",

doi = "10.1109/ASMC.2009.5155966",

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isbn = "9781424436156",

pages = "106--111",

booktitle = "2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009",

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Ragnoli, E, McLoone, S, Lynn, S, Ringwood, J & Macgearailt, N 2009, Identifying key process characteristics and predicting etch rate from High-Dimension Datasets. in 2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009., 5155966, pp. 106-111, 2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009, Berlin, Germany, 10/05/2009. https://doi.org/10.1109/ASMC.2009.5155966

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T1 - Identifying key process characteristics and predicting etch rate from High-Dimension Datasets

AU - Ragnoli, E.

AU - McLoone, S.

AU - Lynn, S.

AU - Ringwood, J.

AU - Macgearailt, N.

PY - 2009/10/27

Y1 - 2009/10/27

N2 - In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.

AB - In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.

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DO - 10.1109/ASMC.2009.5155966

M3 - Conference contribution

AN - SCOPUS:70350227023

SN - 9781424436156

SP - 106

EP - 111

BT - 2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009

T2 - 2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2009

Y2 - 10 May 2009 through 12 May 2009

ER -

Identifying key process characteristics and predicting etch rate from High-Dimension Datasets

Abstract

Conference

ASJC Scopus subject areas

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