Sensor-based Modeling and Monitoring of Chemical Mechanical Polishing | Analysis of Experimental Sensor Data Integrating Statistical Time Series Analysis and Nonlinear Dynamics (Chaos Theory) Paradigms

Prahalada Rao

ISBN 10: 363903564X / ISBN 13: 9783639035643

Published by VDM Verlag Dr. Müller, 2008

About this Item

Sensor-based Modeling and Monitoring of Chemical Mechanical Polishing | Analysis of Experimental Sensor Data Integrating Statistical Time Series Analysis and Nonlinear Dynamics (Chaos Theory) Paradigms | Prahalada Rao | Taschenbuch | Kartoniert | Englisch | 2008 | VDM Verlag Dr. Müller | EAN 9783639035643 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Sensor-based Modeling and Monitoring of Chemical Mechanical Polishing | Analysis of Experimental Sensor Data Integrating Statistical Time Series Analysis and Nonlinear Dynamics (Chaos Theory) Paradigms
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2008
• Language: English
• ISBN 10: 363903564X
• ISBN 13: 9783639035643
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 152H x 11D millimeters 220 millimeters width by 152 millimeters height by 11 millimeters depth
• Weight: 286 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book provides a framework for real time control of the Chemical Mechanical Planarization (CMP) process based on combining nonlinear dynamics principles with statistical process monitoring approaches. CMP has a direct bearing on the computational speed and dimensional characteristics of solid state devices. The challenge in CMP may be narrowed to domains enveloping productivity, measured in terms of material removal rate (MRR), and quality which is usually specified in terms of surface roughness - Ra, within wafer non-uniformity (WIWNU), defect rate, etc. In this work, experimental investigations of CMP are executed with the aid of sensors. The analysis of the data reveals the presence of pronounced stochastic-dynamic characteristics. As a result, we derive a process control method integrating statistical time series analysis and nonlinear dynamics which captures ~ 80% (linear R-sq) of the variation in MRR. In this manner a novel paradigm for effective process control in CMP has been presented.

About the Author

Prahalada is a PhD student at the school of industrial engineering, Oklahoma State University. His research involves sensor based process monitoring and control integrating statistical signal processing techniques with contemporary nonlinear dynamics (chaos theory) paradigms. His PhD is jointly supervised by Drs. Komanduri and Bukkapatnam.