My Account | Careers | Downloads | Blog

PROJECT TITLE :

A model for arsenic anti-site incorporation in GaAs grown by hydride vapor phase epitaxy

ABSTRACT:

GaAs growth by hydride vapor phase epitaxy (HVPE) has regained interest as a potential route to low cost, high efficiency thin film photovoltaics. In order to attain the highest efficiencies, deep level defect incorporation in these materials must be understood and controlled. The arsenic anti-site defect, AsGa or EL2, is the predominant deep level defect in HVPE-grown GaAs. In the present study, the relationships between HVPE growth conditions and incorporation of EL2 in GaAs epilayers were determined. Epitaxial n-GaAs layers were grown under a wide range of deposition temperatures (TD) and gallium chloride partial pressures (PGaCl), and the EL2 concentration, [EL2], was determined by deep level transient spectroscopy. [EL2] agreed with equilibrium thermodynamic predictions in layers grown under conditions in which the growth rate, RG, was controlled by conditions near thermodynamic equilibrium. [EL2] fell below equilibrium levels when RG was controlled by surface kinetic processes, with the disparity increasing as RG decreased. The surface chemical composition during growth was determined to have a strong influence on EL2 incorporation. Under thermodynamically limited growth conditions, e.g., high TD and/or low PGaCl, the surface vacancy concentration was high and the bulk crystal was close to equilibrium with the vapor phase. Under kinetically limited growth conditions, e.g., low TD and/or high PGaCl, the surface attained a high GaCl coverage, blocking As adsorption. This competitive adsorption process reduced the growth rate and also limited the amount of arsenic that incorporated as AsGa. A defect incorporation model which accounted for the surface concentration of arsenic as a function of the growth conditions, was developed. This model was used to identify optimal growth parameters for the growth of thin films for photovoltaics, conditions-
in which a high growth rate and low [EL2] could be attained.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : PRIME: An Optimal Pricing Scheme for Mobile Sensors-as-a-Service ABSTRACT: In this article, we propose a pricing scheme for provisioning mobile Sensors-as-a-Service (mSe-aaS) in the mobile sensor-cloud (MSC) architecture.
PROJECT TITLE : ITrust: An Anomaly-Resilient Trust Model Based on Isolation Forest for Underwater Acoustic Sensor Networks ABSTRACT: Underwater acoustic sensor networks, also known as UASNs, have received a lot of attention as
PROJECT TITLE : Evolving Bipartite Model Reveals the Bounded Weights in Mobile Social Networks ABSTRACT: Users and items in recommendation networks, authors and scientific topics in scholarly networks, male and female in dating
PROJECT TITLE : Predicting Hot Events in the Early Period through Bayesian Model for Social Networks ABSTRACT: It is essential for a wide variety of applications, such as information dissemination mining, ad recommendation, and
PROJECT TITLE : Posterior-neighborhood-regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction ABSTRACT: Because similar users typically have a comparable Quality of Service (QoS) when making use of similar

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry