Line width measurement below 60nm using an optical interferometer and artificial neural network

Chung W. See; Richard J. Smith; Michael Geoffrey Somekh; Andrew Yacoot

doi:10.1117/12.712278

Line width measurement below 60nm using an optical interferometer and artificial neural network

Chung W. See, Richard J. Smith, Michael Geoffrey Somekh, Andrew Yacoot

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.

Original language	English
Title of host publication	Metrology, Inspection, and Process Control for Microlithography XXI
Volume	6518
Edition	PART 1
DOIs	https://doi.org/10.1117/12.712278
Publication status	Published - 15 Oct 2007
Externally published	Yes
Event	Metrology, Inspection, and Process Control for Microlithography XXI - San Jose, CA, United States Duration: 26 Feb 2007 → 1 Mar 2007

Conference

Conference	Metrology, Inspection, and Process Control for Microlithography XXI
Country/Territory	United States
City	San Jose, CA
Period	26/02/07 → 1/03/07

Keywords

Artificial neural network
Interferometer
Line-width measurement
Principal component analysis

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

More information

10.1117/12.712278

Cite this

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title = "Line width measurement below 60nm using an optical interferometer and artificial neural network",

abstract = "We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.",

keywords = "Artificial neural network, Interferometer, Line-width measurement, Principal component analysis",

author = "See, {Chung W.} and Smith, {Richard J.} and Somekh, {Michael Geoffrey} and Andrew Yacoot",

year = "2007",

month = oct,

day = "15",

doi = "10.1117/12.712278",

language = "English",

isbn = "0819466379",

volume = "6518",

booktitle = "Metrology, Inspection, and Process Control for Microlithography XXI",

edition = "PART 1",

note = "Metrology, Inspection, and Process Control for Microlithography XXI ; Conference date: 26-02-2007 Through 01-03-2007",

}

See, CW, Smith, RJ, Somekh, MG & Yacoot, A 2007, Line width measurement below 60nm using an optical interferometer and artificial neural network. in Metrology, Inspection, and Process Control for Microlithography XXI. PART 1 edn, vol. 6518, 65181F, Metrology, Inspection, and Process Control for Microlithography XXI, San Jose, CA, United States, 26/02/07. https://doi.org/10.1117/12.712278

Line width measurement below 60nm using an optical interferometer and artificial neural network. / See, Chung W.; Smith, Richard J.; Somekh, Michael Geoffrey et al.
Metrology, Inspection, and Process Control for Microlithography XXI. Vol. 6518 PART 1. ed. 2007. 65181F.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Line width measurement below 60nm using an optical interferometer and artificial neural network

AU - See, Chung W.

AU - Smith, Richard J.

AU - Somekh, Michael Geoffrey

AU - Yacoot, Andrew

PY - 2007/10/15

Y1 - 2007/10/15

N2 - We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.

AB - We have recently described a technique for optical line-width measurements. The system currently is capable of measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm. The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters. Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that are essential to extend the application of the technique. Two of the factors are signal conditioning and sample classification. Methods, including principal component analysis, that are capable of performing these tasks will be considered.

KW - Artificial neural network

KW - Interferometer

KW - Line-width measurement

KW - Principal component analysis

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DO - 10.1117/12.712278

M3 - Conference article published in proceeding or book

SN - 0819466379

SN - 9780819466372

VL - 6518

BT - Metrology, Inspection, and Process Control for Microlithography XXI

T2 - Metrology, Inspection, and Process Control for Microlithography XXI

Y2 - 26 February 2007 through 1 March 2007

ER -

Line width measurement below 60nm using an optical interferometer and artificial neural network

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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