Multi-way principal component analysis for the endpoint detection of the metal etch process using the whole optical emission spectra

2008 ◽  
Vol 25 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Kyounghoon Han ◽  
Kun Joo Park ◽  
Heeyeop Chae ◽  
En Sup Yoon
Keyword(s):  
Principal Component Analysis ◽  
Emission Spectra ◽  
Principal Component ◽  
Optical Emission ◽  
Component Analysis ◽  
Endpoint Detection ◽  
Optical Emission Spectra

Endpoint Detection in Plasma Etching Using Principal Component Analysis and Expanded Hidden Markov Model

2019 ◽  
Vol 34 (1) ◽  
pp. 943-948 ◽  
Author(s):  
Min-Woo Kim ◽  
Seung-Gyun Kim ◽  
ShuKun Zhao ◽  
Sang Jeen Hong ◽  
Seung-Soo Han
Keyword(s):  
Principal Component Analysis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Plasma Etching ◽  
Hidden Markov ◽  
Principal Component ◽  
Component Analysis ◽  
Endpoint Detection

Emission Spectral Reconstruction Based on Principal Component Analysis Applied to Fluorescence Full-Color Prints

2019 ◽  
Vol 11 (10) ◽  
pp. 1349-1356
Author(s):  
Mi Shang ◽  
Ling Yang ◽  
Danfei Liu ◽  
Zijie Cui ◽  
Yunfei Zhong
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Emission ◽  
Emission Spectra ◽  
Principal Component ◽  
Color Difference ◽  
Component Analysis ◽  
Fluorescence Emission Spectrum ◽  
Color Science ◽  
Full Color ◽  
Spectral Reconstruction

Color reproduction of fluorescent full-color prints depends on many factors, such as preparation of luminescent inks, ratio of luminescent inks to each other, printing technology and so on. In order to make color expression more abundant on fluorescent full-color prints, reconstruction of fluorescence emission spectrum is particularly significant. As opposed to custom methods, principal component analysis has been applied to color science permanently. The method was applied to emission spectral reconstruction in this work and the up-conversion luminescent inks were selected. 336 samples were composed of single ink halftone at a quarter, half, 75%, and 100% surface coverages. The samples were firstly superimposed in one ink and two inks on the blank paper. Moreover, their emission spectral was measured and the procedure for principal component analysis was also performed. The emission spectral was reconstructed by using 1 nm interval from 351 nm to 748 nm. Ultimately, the accuracy of recovery spectral was evaluated through CIEDE2000 color difference evaluation. The obtained results indicated that principal component analysis can be used to reconstruct emission spectra. Besides, the method can also be used for color estimation between different printing materials.

scholarly journals Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
István P. Sugár ◽  
Xiuhong Zhai ◽  
Ivan A. Boldyrev ◽  
Julian G. Molotkovsky ◽  
Howard L. Brockman ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Combination ◽  
Emission Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Lipid Monolayer ◽  
Lateral Organization ◽  
Binary Constituent ◽  
Fluorescent Lipid

Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA) each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions.

Addressing the Sparsity of Laser-Induced Breakdown Spectroscopy Data with Randomized Sparse Principal Component Analysis

2021 ◽  
Author(s):  
Erik Kepes ◽  
Jakub Vrabel ◽  
Pavel Pořízka ◽  
Jozef Kaiser
Keyword(s):  
Principal Component Analysis ◽  
Emission Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensionality ◽  
Laser Induced Breakdown Spectroscopy ◽  
Sparse Principal Component Analysis ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Low Dimensional

Emission spectra yielded by laser-induced breakdown spectroscopy (LIBS) exhibit high dimensionality, redundancy, and sparsity. The high dimensionality is often addressed by principal component analysis (PCA) which creates a low dimensional...

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