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

Verlag: VDM Verlag Dr. Müller, 2008

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a framework for real time controlof the Chemical Mechanical Planarization (CMP)process based on combining nonlinear dynamicsprinciples with statistical process monitoringapproaches. CMP has a directbearing on the computational speed and dimensionalcharacteristics of solid state devices. The challengein CMP may be narrowed to domains envelopingproductivity, measured in terms of material removalrate (MRR), and quality which is usually specified interms of surface roughness - Ra, within wafernon-uniformity (WIWNU), defect rate, etc. In thiswork, experimental investigations of CMP are executedwith the aid of sensors. The analysis of the datareveals the presence of pronounced stochastic-dynamiccharacteristics. As a result, we derive a processcontrol method integrating statistical time seriesanalysis and nonlinear dynamics which captures ~ 80%(linear R-sq) of the variation in MRR. In this mannera novel paradigm for effective process control in CMPhas been presented.

Bestandsnummer des Verkäufers 9783639035643

Bibliografische Details

• Titel: 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
• Verlag: VDM Verlag Dr. Müller
• Veröffentlichungsdatum: 2008
• Sprache: Englisch
• ISBN-10: 363903564X
• ISBN-13: 9783639035643
• Bindung: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.