Improvement of Plasma Etching Endpoint Detection With Data-Driven Wavelength Selection and Gaussian Mixture Model

Author:

Kim Chae Sun1ORCID,Lee Hye Ji1ORCID,Roh Hae Rang1ORCID,Park Taekyoon2ORCID,Lee Yongseok2ORCID,Han Jewoo2,Kwon Sungun2ORCID,Lee Chanmin2,Sun Jongwoo2ORCID,Yoon Kukhan2,Lee Jong Min1ORCID

Affiliation:

1. School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea

2. Memory Process Develpment Team, Samsung Electronics, Pyeongtaek, Republic of Korea

Funder

Samsung Electronics Company Ltd.

Korea Institute for Advancement of Technology (KIAT) Grant

Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Reference22 articles.

1. Low open-area endpoint detection using a PCA-based T/sup 2/ statistic and Q statistic on optical emission spectroscopy measurements

2. Virtual metrology modeling of reactive ion etching based on statistics-based and dynamics-inspired spectral features;chien;J Vac Sci Technol B Nanotechnol Microelectron Mater Process Meas Phenom,2021

3. Spatial characterization of wafer state using principal component analysis of optical emission spectra in plasma etch

4. Robust multi-scale principal components analysis with applications to process monitoring

5. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models;bilmes;International Computer Science Institute,1998

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