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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.
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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.
Biografía del autor
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.
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Sensor-based Modeling and Monitoring of Chemical Mechanical Polishing
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. Bestandsnummer des Verkäufers 151355330
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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
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Taschenbuch. Zustand: Neu. 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
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Sensorbased Modeling and Monitoring of Chemical Mechanical Polishing Analysis of Experimental Sensor Data Integrating Statistical Time Series Analysis and Nonlinear Dynamics Chaos Theory Paradigms
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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
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