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Sensitivity Enhancement of SiO2 Plasma Etching Endpoint Detection Using Modified Gaussian Mixture Model

In this study, the Gaussian mixture model (GMM) was modified and implemented to determine the real-time endpoint of SiO 2 plasma etching using optical emission spectrum analysis. Optical emission spectroscopy (OES) signals were collected from the SiO 2 plasma etching processes, and the modified GMM was applied to SiO 2 etching with relative areas of 8.0, 4.0, and 1.0 %. Consequently, the sensitivity of OES signals was improved by ~5.5 times, and the sensitivity factor of the modified GMM was increased by approximately two times, compared with those of the modified K-means cluster analysis (another clustering technique). In addition, 60 peaks related to the reactants were selected out of 6144 signals to improve the sensitivity of the modified GMM with full-spectrum wavelengths. The modified GMM analysis using the 60 reactant-related peaks exhibited a higher sensitivity (~1.4 times) than that with 6144 full-spectrum OES signals. Thus, the modified GMM can be a suitable and effective clustering technique for etching endpoint detection.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.33 No.2 May 2020

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 252 - 257

Bahasa

English

ISBN/ISSN

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Klasifikasi

-

Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

GAUSSIAN MIXTURE MODEL
PLASMA ETCHING
PLASMA MEASUREMENTS
OPTICAL EMISSION SPECTROSCOPY
ENDPOINT DETECTION

Info Detail Spesifik

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Pernyataan Tanggungjawab

Sangin Lee, Haegyu Jang, Yongjae Kim, Sun Jung Kim, Heeyeop Chae

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