Improvement of Plasma Etching Endpoint Detection With Data-Driven Wavelength Selection and Gaussian Mixture Model
Author:
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
Link
http://xplorestaging.ieee.org/ielx7/66/10209215/10184081.pdf?arnumber=10184081
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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1. Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning;Electronics;2024-09-09
2. Plasma Control: A Review of Developments and Applications of Plasma Medicine Control Mechanisms;Plasma;2024-05-27
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