Model-Based Corona Charge - Kelvin Probe Characterization of Patterned Structures

2018 ◽  
Vol 85 (6) ◽  
pp. 97-104
Author(s):  
Dmitriy Marinskiy ◽  
Jacek Lagowski
Keyword(s):  
Kelvin Probe ◽  
Model Based ◽  
Patterned Structures ◽  
Corona Charge

Pioneering Application of Corona Charge-Kelvin Probe Metrology to Noncontact Characterization of In0.53 Ga0.47 As/Al2O3/HfO2 Stack

2014 ◽  
Vol 1691 ◽  
Author(s):  
Alexandre Savtchouk ◽  
John D’Amico ◽  
Marshall Wilson ◽  
Jacek Lagowski ◽  
Wei-E Wang ◽  
...  
Keyword(s):  
High Mobility ◽  
Dielectric Surface ◽  
Differential Capacitance ◽  
Voltage Response ◽  
Induced Voltage ◽  
Kelvin Probe ◽  
Dielectric Characterization ◽  
High K ◽  
Corona Charge

ABSTRACTWe report the first successful application of corona charging noncontact C-V and I-V metrology to interface and dielectric characterization of high-k/III-V structures. The metrology, which has been commonly used in Si IC manufacturing, uses incremental corona charge dosing, ΔQC, on the dielectric surface, and the measurement of surface voltage response, ΔVS, using a Kelvin-probe. Its application to In0.53Ga0.47As with a high-k stack required modifications related to the effects of dielectric trap induced voltage transients. The developed Corona Charge-Kelvin Probe Metrology adopted strictly differential measurements using ΔQC and ΔV, and corresponding differential capacitance rather than measurements based on total global charge, Q, and voltage, V, values.Electrical characterization data including interface trap density, electrical oxide thickness, and dielectric leakage are presented for a sample containing an In0.53 Ga0.47 As channel overlaid with a bilayer (2nm Al2O3/5nm HfO2) dielectric stack that is considered to be very promising for application in performance NFETs with high-mobility channels.

scholarly journals p-box: a new graph model

2015 ◽  
Vol Vol. 17 no. 1 (Graph Theory) ◽  
Author(s):  
Mauricio Soto ◽  
Christopher Thraves-Caro
Keyword(s):  
Graph Theory ◽  
Graph Model ◽  
Directed Path ◽  
Outerplanar Graphs ◽  
New Model ◽  
Model Based ◽  
Representative Element ◽  
International Audience ◽  
Graph Families

Graph Theory International audience In this document, we study the scope of the following graph model: each vertex is assigned to a box in ℝd and to a representative element that belongs to that box. Two vertices are connected by an edge if and only if its respective boxes contain the opposite representative element. We focus our study on the case where boxes (and therefore representative elements) associated to vertices are spread in ℝ. We give both, a combinatorial and an intersection characterization of the model. Based on these characterizations, we determine graph families that contain the model (e. g., boxicity 2 graphs) and others that the new model contains (e. g., rooted directed path). We also study the particular case where each representative element is the center of its respective box. In this particular case, we provide constructive representations for interval, block and outerplanar graphs. Finally, we show that the general and the particular model are not equivalent by constructing a graph family that separates the two cases.

Characterization of a commercial hybrid iterative and model-based reconstruction algorithm in radiation oncology

2014 ◽  
Vol 41 (8Part1) ◽  
pp. 081907 ◽  
Author(s):  
Ryan G. Price ◽  
Sean Vance ◽  
Richard Cattaneo ◽  
Lonni Schultz ◽  
Mohamed A. Elshaikh ◽  
...  
Keyword(s):  
Radiation Oncology ◽  
Reconstruction Algorithm ◽  
Model Based

Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches

2021 ◽  
pp. 229-248
Author(s):  
Carlos A. Santos Silva ◽  
Manar Amayri ◽  
Kaustav Basu
Keyword(s):  
Energy Demand ◽  
Data Driven ◽  
Energy Services ◽  
Model Based

Classification Trees as Proxies

2015 ◽  
Vol 2 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Anthony Scime ◽  
Nilay Saiya ◽  
Gregg R. Murray ◽  
Steven J. Jurek
Keyword(s):  
Data Analysis ◽  
Classification Trees ◽  
Original Model ◽  
Classification Model ◽  
Model Based ◽  
Alternative Characterization

In data analysis, when data are unattainable, it is common to select a closely related attribute as a proxy. But sometimes substitution of one attribute for another is not sufficient to satisfy the needs of the analysis. In these cases, a classification model based on one dataset can be investigated as a possible proxy for another closely related domain's dataset. If the model's structure is sufficient to classify data from the related domain, the model can be used as a proxy tree. Such a proxy tree also provides an alternative characterization of the related domain. Just as important, if the original model does not successfully classify the related domain data the domains are not as closely related as believed. This paper presents a methodology for evaluating datasets as proxies along with three cases that demonstrate the methodology and the three types of results.

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