Spectroscopic Ellipsometry of Nanoscale Materials for Semiconductor Device Applications

2013 ◽  
pp. 557-581 ◽  
Author(s):  
Alain C. Diebold ◽  
Florence J. Nelson ◽  
Vimal K. Kamineni
Keyword(s):  
Spectroscopic Ellipsometry ◽  
Semiconductor Device ◽  
Nanoscale Materials ◽  
Device Applications

Defects in β-SiC thin films grown on α-SiC substrates

1988 ◽  
Vol 46 ◽  
pp. 568-569
Author(s):  
Karren L. More
Keyword(s):  
Thin Films ◽  
Semiconductor Device ◽  
Lattice Mismatch ◽  
Chemical Vapor ◽  
Substrate Material ◽  
Growth Interface ◽  
Si Substrates ◽  
Thermal Expansion Coefficients ◽  
Device Applications ◽  
Expansion Coefficients

Beta-SiC is an ideal candidate material for use in semiconductor device applications. Currently, monocrystalline β-SiC thin films are epitaxially grown on {100} Si substrates by chemical vapor deposition (CVD). These films, however, contain a high density of defects such as stacking faults, microtwins, and antiphase boundaries (APBs) as a result of the 20% lattice mismatch across the growth interface and an 8% difference in thermal expansion coefficients between Si and SiC. An ideal substrate material for the growth of β-SiC is α-SiC. Unfortunately, high purity, bulk α-SiC single crystals are very difficult to grow. The major source of SiC suitable for use as a substrate material is the random growth of {0001} 6H α-SiC crystals in an Acheson furnace used to make SiC grit for abrasive applications. To prepare clean, atomically smooth surfaces, the substrates are oxidized at 1473 K in flowing 02 for 1.5 h which removes ∽50 nm of the as-grown surface. The natural {0001} surface can terminate as either a Si (0001) layer or as a C (0001) layer.

Thin germanium-carbon alloy layers grown directly on silicon for metal-oxide-semiconductor device applications

2006 ◽  
Vol 88 (15) ◽  
pp. 152101 ◽  
Author(s):  
D. Q. Kelly ◽  
I. Wiedmann ◽  
J. P. Donnelly ◽  
S. V. Joshi ◽  
S. Dey ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Semiconductor Device ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Device Applications

Development of New Complex Machining Technology for Single Crystal Silicon Carbide Polishing

2016 ◽  
Vol 10 (5) ◽  
pp. 786-793
Author(s):  
Tsuneo Kurita ◽  
◽  
Koji Miyake ◽  
Kenji Kawata ◽  
Kiwamu Ashida ◽  
...  
Keyword(s):  
Silicon Carbide ◽  
Single Crystal ◽  
Semiconductor Device ◽  
Single Crystal Silicon ◽  
High Hardness ◽  
Electrochemical Technique ◽  
Crystal Silicon ◽  
Abrasive Particles ◽  
Power Semiconductor ◽  
Device Applications

Single-crystal, silicon carbide (SiC) wafers surpass silicon in terms of voltage resistance and heat resistance, and show promise for use in power semiconductor device applications. The aim of this research is to develop a complex machining technology for SiC, which is known to be difficult to process owing to its high hardness. This paper proposes a complex machining method based on converting SiC into a material with a relatively low hardness, and then polishing it using abrasive particles with a higher hardness. The proposed polishing method uses either a photodissociation or an electrochemical technique to reduce the hardness of SiC. The effectiveness of the combined technique is experimentally demonstrated. In addition, a method is proposed for monitoring the processing state by measuring the electric current.

Characterization of SixGe1−x/Si heterostructures for device applications using spectroscopic ellipsometry

1993 ◽  
Vol 74 (1) ◽  
pp. 586-595 ◽  
Author(s):  
R. M. Sieg ◽  
S. A. Alterovitz ◽  
E. T. Croke ◽  
M. J. Harrell ◽  
M. Tanner ◽  
...  
Keyword(s):  
Spectroscopic Ellipsometry ◽  
Device Applications

scholarly journals Bandgap prediction of two-dimensional materials using machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255637
Author(s):  
Yu Zhang ◽  
Wenjing Xu ◽  
Guangjie Liu ◽  
Zhiyong Zhang ◽  
Jinlong Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semiconductor Device ◽  
2D Materials ◽  
Support Vector ◽  
Material Structure ◽  
Two Dimensional ◽  
Promising Alternative ◽  
Two Dimensional Materials ◽  
Device Applications ◽  
Boosted Decision Trees

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

scholarly journals Amorphous Metallizations for High-Temperature Semiconductor Device Applications

1982 ◽  
Vol IE-29 (2) ◽  
pp. 154-157 ◽  
Author(s):  
J. D. Wiley ◽  
J. H. Perepezko ◽  
J. E. Nordman ◽  
Kang-Jin Guo
Keyword(s):  
High Temperature ◽  
Semiconductor Device ◽  
Device Applications
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