scholarly journals Determination of BPSG Thin-Film Properties using IR Reflection Spectroscopy of Product Wafers

1993 ◽  
Vol 324 ◽  
Author(s):  
Thomas M. Niemczyk ◽  
James E. Franke ◽  
Lizhong Zhang ◽  
David M. Haaland ◽  
Kenneth J. Radigan
Keyword(s):  
Film Thickness ◽  
Boron Content ◽  
Phosphorus Content ◽  
Large Data ◽  
Film Properties ◽  
Data Set ◽  
Partial Least Squares Analysis ◽  
Large Data Set ◽  
Calibration Sets

AbstractThree BPSG calibration sets were analyzed using infrared external reflection-absorption spectroscopy: 1) 21 films deposited on undoped silicon coated with 0.1 pm of silicon dioxide, 2) 21 films deposited on undoped silicon, and 3) 9 films deposited on microelectronics product wafers. A multivariate partial least squares analysis of the spectral data for the first large data set showed that boron content, phosphorus content, and film thickness can be quantified with precisions of 0.10 wt%, 0.12 wt%, and 30 Å, respectively. The second large data set yielded similar results. The precisions obtained for the nine product wafer samples were 0.13 wt% B, 0.09 wt% P, and for film thickness 103 Å.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

Main large data set features detection by a linear predictor model

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  
Keyword(s):  
Large Data ◽  
Data Set ◽  
Linear Predictor ◽  
Large Data Set ◽  
Predictor Model

Governance Problems in Closely Held Corporations

2011 ◽  
Vol 46 (4) ◽  
pp. 943-966 ◽  
Author(s):  
Venky Nagar ◽  
Kathy Petroni ◽  
Daniel Wolfenzon
Keyword(s):  
Ownership Structure ◽  
Large Data ◽  
Minority Shareholders ◽  
Return On Assets ◽  
Data Set ◽  
Large Data Set ◽  
Ownership Rights ◽  
Shared Ownership

AbstractA major governance problem in closely held corporations is the majority shareholders’ expropriation of minority shareholders. As a solution, legal and finance research recommends that the main shareholder surrender some control to minority shareholders via ownership rights. We test this proposition on a large data set of closely held corporations. We find that shared-ownership firms report a substantially larger return on assets and lower expense-to-sales ratios. These findings are robust to institutionally motivated corrections for endogeneity of ownership structure. We provide evidence on the presence of governance problems and the effectiveness of shared ownership as a solution in settings characterized by illiquidity of ownership.

A strategy for automatic moderation of a large data set of users comments

2014 ◽  
Author(s):  
Marcos Rodrigues Saude ◽  
Marcelo de Medeiros Soares ◽  
Henrique Gomes Basoni ◽  
Patrick Marques Ciarelli ◽  
Elias Oliveira
Keyword(s):  
Large Data ◽  
Data Set ◽  
Large Data Set
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