Combining Models and Guided Empirical Search to Optimize for Multiple Levels of the Memory Hierarchy

2005 ◽  
Author(s):  
Chun Chen ◽  
J. Chame ◽  
M. Hall
Keyword(s):  
Memory Hierarchy ◽  
Multiple Levels ◽  
Empirical Search

Particle characterization of CVD tungsten metallization for 64 MBIT DRAM

1991 ◽  
Vol 49 ◽  
pp. 896-897
Author(s):  
Marylyn Bennett-Lilley ◽  
Thomas T.H. Fu ◽  
David D. Yin ◽  
R. Allen Bowling
Keyword(s):  
Chemical Vapor ◽  
Device Performance ◽  
Particle Characterization ◽  
Particle Generation ◽  
Tungsten Hexafluoride ◽  
Homogeneous Particle ◽  
Multiple Levels ◽  
Circuit Density ◽  
Sources Of Contamination

Chemical Vapor Deposition (CVD) tungsten metallization is used to increase VLSI device performance due to its low resistivity, and improved reliability over other metallization schemes. Because of its conformal nature as a blanket film, CVD-W has been adapted to multiple levels of metal which increases circuit density. It has been used to fabricate 16 MBIT DRAM technology in a manufacturing environment, and is the metallization for 64 MBIT DRAM technology currently under development. In this work, we investigate some sources of contamination. One possible source of contamination is impurities in the feed tungsten hexafluoride (WF6) gas. Another is particle generation from the various reactor components. Another generation source is homogeneous particle generation of particles from the WF6 gas itself. The purpose of this work is to investigate and analyze CVD-W process-generated particles, and establish a particle characterization methodology.

Survey Actioning: Driving Positive Change at Multiple Levels

2011 ◽  
Author(s):  
Carly S. Bruck ◽  
Rita Williams ◽  
Tripp Welch ◽  
Phil Warden ◽  
Patrick K. Hyland
Keyword(s):  
Positive Change ◽  
Multiple Levels

Evaluating the etiology of climate: Building understanding from multiple levels

2003 ◽  
Author(s):  
Chera L. Haworth ◽  
Andrea F. Snell ◽  
Daniel J. Svyantek ◽  
Gary A. Kustis
Keyword(s):  
Multiple Levels

Learning at Multiple Levels of Abstraction: The Case of Verb Argument Constructions

2007 ◽  
Author(s):  
Amy Perfors ◽  
Charles Kemp ◽  
Elizabeth Wonnacott ◽  
Joshua B. Tenenbaum
Keyword(s):  
Levels Of Abstraction ◽  
Multiple Levels

Multiple Levels of Crowding

2014 ◽  
Author(s):  
Ruth Kimchi ◽  
Yossef Pirkner
Keyword(s):  
Multiple Levels

Review of Flap Monitoring Technology in 2020

2020 ◽  
Vol 36 (06) ◽  
pp. 722-726
Author(s):  
Adam Jacobson ◽  
Oriana Cohen
Keyword(s):  
Head And Neck ◽  
Free Flap ◽  
Loss Rate ◽  
Early Recognition ◽  
Postoperative Patient ◽  
Free Flap Monitoring ◽  
Improved Outcomes ◽  
Vascular Compromise ◽  
Flap Monitoring ◽  
Multiple Levels

AbstractAdvances in free flap reconstruction of complex head and neck defects have allowed for improved outcomes in the management of head and neck cancer. Technical refinements have decreased flap loss rate to less than 4%. However, the potential for flap failure exists at multiple levels, ranging from flap harvest and inset to pedicle lay and postoperative patient and positioning factors. While conventional methods of free flap monitoring (reliant on physical examination) remain the most frequently used, additional adjunctive methods have been developed. Herein we describe the various modalities of both invasive and noninvasive free flap monitoring available to date. Still, further prospective studies are needed to compare the various invasive and noninvasive technologies and to propel innovations to support the early recognition of vascular compromise with the goal of even greater rates of flap salvage.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Sign in / Sign up

Export Citation Format

Share Document