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Neuronale Netzwerkmodelle zur Analyse hochdimensionaler, multisensorischer Datensätze prozessierter Si-Wafer

URN to cite this document: urn:nbn:de:bvb:355-opus-548

Schels, Armin (2002) Neuronale Netzwerkmodelle zur Analyse hochdimensionaler, multisensorischer Datensätze prozessierter Si-Wafer. PhD, Universität Regensburg

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Abstract (German)

In dieser Arbeit werden verschiedene neuronale Netzwerkmodelle
zur Analyse hochdimensionaler multisensorischer Datensätze
prozessierter Silizium-Wafer untersucht.
Für die Dimensionsreduzierung und Kennzahlenextraktion werden
Methoden der Principal Component Analysis (PCA),
Independent Component Analysis (ICA) und Backpropagation-Netzwerke
verwendet. Zur anschließenden Klassifikation der Datensätze
werden Kohonen Self Organizing Feature Maps (SOM),
RBF-Netze (Radial Basis Function) und Growing Neural Gases (GNG)
benützt. Ziel der Analyse ist die frühzeitige Fehlererkennung und
Klassifikation abnormaler Prozessverläufe und damit die Steigerung
der Produktivität und Produktqualität in der Halbleiterfertigung.

Translation of the abstract (English)

Different neural network architectures were used for the analysis
of high-dimensional and multi-sensoric datasets of processed silicon
wafers. For dimensionality reduction and feature extraction layered
networks with learning rules implementing a Principal Component
Analysis (PCA), an Independent Component Analysis (ICA) and an
error-backpropagation method were used. For the classification task
Kohonen�s Self Organizing Feature Maps (SOM), RBF-Networks
(Radial Basis Function) and Growing Neural Gases (GNG) were used.
The goal of these analyses was an online fault detection and a
classification of abnormal processes resulting in an improved
productivity and product quality in the semiconductor industry.

Item Type:	Thesis of the University of Regensburg (PhD)
Referee:	Elmar W. Lang
Date of exam:	06 December 2001
Institutions:	Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Keywords:	Neuronales Netz , Zeitreihenanalyse , Halbleiterindustrie , , neural networks , fault detection , time series analysis
Subjects:	500 Science > 570 Life sciences
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Owner:	Universitätsbibliothek Regensburg
Deposited On:	21 Oct 2009 15:39
Last Modified:	22 Oct 2012 09:06
Item ID:	9899

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