Journal ArticleDOI
Partial X-ray photoelectron spectroscopy to constructing neural network model of plasma etching surface
Byungwhan Kim,Woo Suk Kim +1 more
TLDR
In this article, a backpropagation neural network (BPNN) with X-ray photoelectron spectroscopy (XPS) was used to predict surface roughness of silicon carbide films at NF"3 inductively coupled plasma.About:
This article is published in Microelectronic Engineering.The article was published on 2007-04-01. It has received 8 citations till now. The article focuses on the topics: Plasma etching & X-ray photoelectron spectroscopy.read more
Citations
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Journal ArticleDOI
Optimization of Principal-Component-Analysis-Applied in Situ Spectroscopy Data Using Neural Networks and Genetic Algorithms
Byungwhan Kim,Min Ji Kwon +1 more
TL;DR: A new model of multidimensional in situ diagnostic data is presented by combining a back-propagation neural network (BPNN), principal component analysis (PCA), and a genetic algorithm (GA).
Journal ArticleDOI
Exponentially weighted moving average-based procedure with adaptive thresholding for monitoring nonlinear profiles: Monitoring of plasma etch process in semiconductor manufacturing
TL;DR: Wavelet-based exponentially weighted moving average (EWMA) test statistic with adaptive thresholding method, which extracts several significant coefficients from original functional data in the wavelet domain and monitors out-of-control events, is proposed and local shift monitoring with functional data is more significant than the detection of global shifting patterns.
Journal ArticleDOI
Prediction of silicon dry etching using a piecewise linear algorithm
Victor Manuel Jimenez-Fernandez,C. Reyes-Betanzo,M. Angelica-Cerdan,Z.J. Hernandez-Paxtian,Hector Vazquez-Leal,A. Itzmoyotl-Toxqui +5 more
TL;DR: In this paper, a piecewise linear algorithm for predicting silicon etch rates in fluorine-based plasmas is presented, where discrete experimental data of pressure and RF power in reactive ion etching are used to construct a set of local two-dimensional etching functions that serve as a basis for computing numerical solutions.
Journal ArticleDOI
Functional Kernel-Based Modeling of Wavelet Compressed Optical Emission Spectral Data: Prediction of Plasma Etch Process
TL;DR: A kernel-based process model, consisting of kernel partial least squares regression and kernel ridge regression, is used to model etch rate and uniformity in a plasma etch process and exhibits an improved prediction over NNs and linear-based models.
Journal ArticleDOI
Use of neural network to model X-ray photoelectron spectroscopy data for diagnosis of plasma etch equipment
TL;DR: In this study, a prediction model is constructed by combining XPS and backpropagation neural network (BPNN) to improve the BPNN prediction performance, and GA-BPNN models yielded an improved prediction with respect to conventional BPNN and statistical regression models.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
A general regression neural network
TL;DR: The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure that provides smooth transitions from one observed value to another.
Journal ArticleDOI
Universal approximation using radial-basis-function networks
Jooyoung Park,Irwin W. Sandberg +1 more
TL;DR: It is proved thatRBF networks having one hidden layer are capable of universal approximation, and a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.
Book
A User's Guide to Principal Components
TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
Related Papers (5)
Modeling plasma etching process using a radial basis function network
Byungwhan Kim,Kyungyoung Park +1 more
Qualitative modeling of silica plasma etching using neural network
Byungwhan Kim,Kwang-Ho Kwon +1 more
GA-optimized backpropagation neural network with multi-parameterized gradients and applications to predicting plasma etch data
Byungwhan Kim,Sungmo Kim +1 more