An Examination of the Mechanisms of Si Diffusion in GaAs

1989 ◽  
Vol 163 ◽  
Author(s):  
Shaofeng Yu ◽  
Ulrich M. Gosele ◽  
Teh Y. Tan
Keyword(s):  
Fermi Level ◽  
Point Defect ◽  
Essential Role ◽  
Defect Concentration ◽  
Experimental Results ◽  
Quantitative Models ◽  
Effect Mechanism ◽  
Level Effect

AbstractAn examination of the three available quantitative models of Si diffusion in GaAs has led to the conclusion that the Fermi-level effect mechanism plays the most essential role. In some experimental results a point defect concentration transient is involved which should be incoorporated in future models.

Mechanisms of Doping-Enhanced Superlattice Disordering and of Gallium Self-Diffusion in GaAs

1987 ◽  
Vol 104 ◽  
Author(s):  
T. Y. Tan ◽  
U. Gösele ◽  
B. P. R. Marioton
Keyword(s):  
Fermi Level ◽  
Point Defect ◽  
Dopant Diffusion ◽  
Self Diffusion ◽  
N Doping ◽  
Charged Point Defect ◽  
Charged Point ◽  
Defect Species ◽  
Level Effect ◽  
Positively Charged

ABSTRACTRecently available Ga-Al interdiffusion results in GaAs/AlAs superlattices allow to conclude that Ga self-diffusion in GaAs is carried by triply-negatively charged Ga vacancies under intrinsic and n-doping conditions. The mechanism of the Si enhanced superlattice disordering is the Fermi-level effect which increases the concentrations of the charged point defect species. For the effect of the p-dopants Be and Zn, the Fermi-level effect has to be considered together with dopant diffusion induced Ga self-interstitial supersaturation or undersaturation. Self-diffusion of Ga in GaAs under heavy p-doping conditions is governed by positively charged Ga self-interstitials.

Simulation of Under- and Supersaturation of Gallium Vacancies in Gallium Arsenide During Silicon in- and Outdiffusion

1997 ◽  
Vol 490 ◽  
Author(s):  
C.-H. Chen ◽  
U. Gösele ◽  
T. Y. Tan
Keyword(s):  
Gallium Arsenide ◽  
Fermi Level ◽  
Point Defect ◽  
Surface States ◽  
Effect Model ◽  
N Doping ◽  
Defect Species ◽  
Concentration Of Electrons ◽  
Level Effect ◽  
Doping Condition

ABSTRACTThe diffusivity of Si in GaAs shows a dependence on the cubic power of its concentration or the concentration of electrons n under both in- and outdiffusion conditions. Hence, the diffusion of Si in GaAs is consistent with the Fermi-level effect model invoking the triply-negatively-charged Ga vacancies, , as the point defect species responsible for diffusion to occur on the Ga sublattice under n-doping condition. However, the Si diffusivity values of the indiffusion cases is several orders of magnitude smaller than those of the outdiffusion cases at the same Si concentrations. This means that the two apparent Si diffusivity values under intrinsic conditions will contain also the same discrepancy, which has been previously assessed to be due to a undersaturation in indiffusion cases and a supersaturation in outdiffusion cases. In this study we have calculated the under- and supersaturation values using the known Si diffusivities. We found that the GaAs surface states play a key role in the development of the under- and supersaturations.

scholarly journals A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 359
Author(s):  
Kai Ye ◽  
Yangheran Piao ◽  
Kun Zhao ◽  
Xiaohui Cui
Keyword(s):  
Decision Making ◽  
Essential Role ◽  
Online Discussion ◽  
Professional Community ◽  
Experimental Results ◽  
Agricultural Products ◽  
Price Prediction ◽  
Price Data ◽  
Rich Information ◽  
Lstm Network

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

Fermi-Level Effect on Group III Atom Interdiffusion in III-V Compounds: Bandgap Heterogeneity and Low Silicon-Doping

1997 ◽  
Vol 490 ◽  
Author(s):  
C.-H. Chen ◽  
U. Gösele ◽  
T. Y. Tan
Keyword(s):  
Fermi Level ◽  
Equilibrium Concentration ◽  
Electric Field Effect ◽  
Silicon Doping ◽  
Group Iii ◽  
Semiconductor Superlattice ◽  
Effect Model ◽  
N Doping ◽  
Short Annealing ◽  
Level Effect

ABSTRACTHeavy n-doping enhanced disordering of GaAs based III-V semiconductor superlattice or quantum well layers, as well as the diffusion of Si in GaAs have been previously explained by the Fermi-level effect model with the triply-negatively-charged group III lattice vacancies identified to be the responsible point defect species. These vacancies have a thermal equilibrium concentration proportional to the cubic power of the electron concentration n, leading to the same dependence of the layer disordering rate. In this paper, in addition, we take into account also the electric field effect produced by the material bandgap heterogeneity and/or hetero-junctions. In heavily n-doped or long time annealing cases, this effect is negligible. At low n-doping levels and for short annealing times, the layer disordering rate can be enhanced or reduced by this effect. Available experimental results of low Si-doped and very short-time annealed samples have been satisfactorily fitted using the Fermi-level effect model.

scholarly journals Calculation of point defect concentration in Cu2ZnSnS4: Insights into the high-temperature equilibrium and quenching

2017 ◽  
Vol 122 (3) ◽  
pp. 035707 ◽  
Author(s):  
V. Kosyak ◽  
A. V. Postnikov ◽  
J. Scragg ◽  
M. A. Scarpulla ◽  
C. Platzer-Björkman
Keyword(s):  
High Temperature ◽  
Point Defect ◽  
Defect Concentration ◽  
Temperature Equilibrium
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