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Virtual Metrology for Etch Profile in Silicon Trench Etching With SF₆/O₂/Ar Plasma

This study practiced virtual metrology (VM) for the etch profile and depth in the deep silicon trench etching with SF 6 /O 2 /Ar plasma. Machine learning-based VM models constitute the classification models of etch profile and the prediction models of etch depth from the silicon trench etch. Machine learning algorithms of random forest and Xgboost were used for classifying etch profiles by employing recipe-based equipment status variable identification (SVID) data. Predictive etch depth models constructed with neural network models employed both equipment SVID data and optical emission spectroscopy (OES) data, which provide chemistry information of the plasma during the etch process. Plasma VM model, augmenting OES data to SVID data presented improved accuracy in predicting etch profile. The augmented phenomenological plasma information during the etch process helped to establish a more accurate VM model in the plasma process. The importance of variables was identified through the permutation importance of each model. Additionally, the actual process results of the variables with high importance were analyzed with the etching reaction of SF 6 /O 2 /Ar plasma.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.35 No.1 February 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 128-136

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
VIRTUAL METROLOGY
ETCH PROFILE
ETCH DEPTH
SI ETCHING

Info Detail Spesifik

DOI: 10.1109/TSM.2021.3138918

Pernyataan Tanggungjawab

Jeong Eun Choi, Hyoeun Park, Yongho Lee, Sang Jeen Hong

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