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Improved Measurement of Thin Film Thickness in Spectroscopic Reflectometer Using Convolutional Neural Networks

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Abstract

This research introduces a novel method of ensuring more reliable measurement of thin film thickness in spectroscopic reflectometer. Nonlinear fitting is the method most commonly used for measuring thin film thickness; however, it runs into the problem of a local minimum, which entails ambiguity. To improve measurement, prior to analysis of spectral reflectance profiles using nonlinear fitting, initial thickness value is estimated based on convolutional neural networks. Due to the supportive role of convolutional neural networks, thin film thickness can be determined without ambiguity.

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Abbreviations

\(d\) :

Thickness of thin film

\(k\) :

Spectral wavenumber

\(R\) :

Total reflection coefficient

\(r_{12}\) :

Fresnel reflection coefficients of top boundaries of thin films

\(r_{23}\) :

Fresnel reflection coefficients of bottom boundaries of thin films

\(N\) :

Refractive index of thin films

\(R_{ref}\) :

Spectral reflectance of reference specimen

\(G_{ref}\) :

Spectral density of reference specimen

\(G_{sam}\) :

Spectral density of unknown film thickness sample

\(R_{sam}\) :

Spectral reflectance of unknown film thickness

\(\theta_{1}\) :

Angle of incidence

\(\theta_{2}\) :

Angle of refraction

\(w\) :

Value of the kernel

\(v\) :

Value of feature map

\(b\) :

Additive bias

\(p\) :

Position in a kernel

\(x\) :

Position of unit in a feature map

\(i\) :

Layer of a feature map

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea

    Min-Gab Kim

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  1. Min-Gab Kim
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Correspondence to Min-Gab Kim.

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Kim, MG. Improved Measurement of Thin Film Thickness in Spectroscopic Reflectometer Using Convolutional Neural Networks. Int. J. Precis. Eng. Manuf. 21, 219–225 (2020). https://doi.org/10.1007/s12541-019-00260-4

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  • DOI: https://doi.org/10.1007/s12541-019-00260-4

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