๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Modeling of plasma process data using a multi-parameterized generalized regression neural network

โœ Scribed by Byungwhan Kim; Minji Kwon; Sang Hee Kwon


Publisher
Elsevier Science
Year
2009
Tongue
English
Weight
183 KB
Volume
86
Category
Article
ISSN
0167-9317

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Prediction of plasma etch process by usi
โœ Byungwhan Kim; Minji Kwon ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 395 KB

Optical emission spectroscopy (OES) data were used to construct neural network models of plasma etch process. According to a statistical experiment, actinomeric OES data were collected from the etching of oxide thin films in a CHF 3 -CF 4 magnetically enhanced reactive ion etching system. The etch r

Run-to-run process control of a plasma e
โœ Jill P. Card; Mark Naimo; William Ziminsky ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 250 KB ๐Ÿ‘ 2 views

Run-to-run control of a plasma etch process for 8 inch diameter silicon wafers at Digital Semiconductor is determined by maintenance of targeted values of post-etch metrology variables. The post-etch quality variables are extremely sensitive to variation in the etch chamber conditions due to fluctua

Modelling of hot strip rolling process u
โœ H.J. Kim; M. Mahfouf; Y.Y. Yang ๐Ÿ“‚ Article ๐Ÿ“… 2008 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 931 KB

A neural network-based approach is developed to predict a mechanical property for the hot-rolled alloy strip. Using a data set containing critical information on the mechanical property which was obtained from a POSCO hot strip mill, a neural network-based model is elicited. A compact set of process

Affine modeling of nonlinear multivariab
โœ Amin Sabet Kamalabady; Karim Salahshoor ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 786 KB

This paper presents a new method for on-line identification of exact affine model for multivariable processes with nonlinear and time-varying behaviors. A self-generating radial basis function (RBF) neural network trained by growing and pruning algorithm for RBF (GAP-RBF) is utilized for deriving th