Efficient Visual Mixture Design of Experiments using Property Clustering Techniques

2009 ◽  
Vol 48 (4) ◽  
pp. 2245-2256 ◽  
Author(s):  
Charles C. Solvason ◽  
Nishanth G. Chemmangattuvalappil ◽  
Fadwa T. Eljack ◽  
Mario R. Eden
Keyword(s):  
Design Of Experiments ◽  
Mixture Design ◽  
Clustering Techniques ◽  
Property Clustering

Targeting optimum resource allocation using reverse problem formulations and property clustering techniques

2005 ◽  
Vol 29 (11-12) ◽  
pp. 2304-2317 ◽  
Author(s):  
Fadwa T. Eljack ◽  
Ahmed F. Abdelhady ◽  
Mario R. Eden ◽  
Frederico B. Gabriel ◽  
Xiaoyun Qin ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Clustering Techniques ◽  
Reverse Problem ◽  
Property Clustering ◽  
Problem Formulations

Simultaneous Process and Molecular Design through Property Clustering Techniques:  A Visualization Tool

2007 ◽  
Vol 46 (10) ◽  
pp. 3400-3409 ◽  
Author(s):  
Vasiliki Kazantzi ◽  
Xiaoyun Qin ◽  
Mahmoud El-Halwagi ◽  
Fadwa Eljack ◽  
Mario Eden
Keyword(s):  
Molecular Design ◽  
Visualization Tool ◽  
Clustering Techniques ◽  
Simultaneous Process ◽  
Property Clustering

Clustering techniques for thyroid nodules malignancy inference in the era of personalized medicine

2020 ◽  
Author(s):  
Andrea Giani ◽  
de Souza Patricia Borges ◽  
Stefania Bartoletti ◽  
Flavio Morselli ◽  
Andrea Conti ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Thyroid Nodules ◽  
Clustering Techniques

A State of Art Approaches on Energy Efficient Clustering Techniques in WSN

2019 ◽  
Vol 7 (3) ◽  
pp. 50-54
Author(s):  
N. Thilagavathi ◽  
Christy Wood ◽  
V. Hemalakshumi ◽  
V. Mathumiithaa
Keyword(s):  
Energy Efficient ◽  
Clustering Techniques ◽  
Energy Efficient Clustering ◽  
State Of Art

Systematic Defect Identification through Layout Snippet Clustering

2010 ◽  
Author(s):  
Wing Chiu Tam ◽  
Osei Poku ◽  
R. D. (Shawn) Blanton
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Yield Loss ◽  
Defect Identification ◽  
Clustering Techniques ◽  
Dominant Component

Abstract Systematic defects due to design-process interactions are a dominant component of integrated circuit (IC) yield loss in nano-scaled technologies. Test structures do not adequately represent the product in terms of feature diversity and feature volume, and therefore are unable to identify all the systematic defects that affect the product. This paper describes a method that uses diagnosis to identify layout features that do not yield as expected. Specifically, clustering techniques are applied to layout snippets of diagnosis-implicated regions from (ideally) a statistically-significant number of IC failures for identifying feature commonalties. Experiments involving an industrial chip demonstrate the identification of possible systematic yield loss due to lithographic hotspots.

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