Silicon Carbide devices are proving to be most promising for high power and high-temperature application in recent times. Efficient and accurate characterization of the device characteristics is key to the fabrication of high quality devices and reproduction of the quality of the devices fabricated. Capacitance-Voltage profiling is one of the most commonly used techniques to measure the doping profiles of semiconductors. However, interpretation of C-V profiling in the presence of traps in the material becomes complicated. Various complications arising from compensation between donors and acceptors, partial ionization of dopants and presence of deep level impurities could yield anomalous measured profile. Silicon Carbide being a wide bandgap semiconductor, many impurities commonly found such as Boron and Aluminum are not completely ionized at Room temperature. This leads to complications in calculating doping profiles when the trap levels are deeper. Other complications arising due to series resistance effect and diode edge effect may also affect the measured profile. Accounting for these complications may be difficult by mere observation of the measured profile. Simulation can be an excellent tool to extract parameters of interest from experimental results that are influenced by non-ideal effects. Fitting of the experimentally obtained data with simulated profile using specific models may be a useful technique to quantitatively account for the deviations from the actual profiles.

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<p> The integration of silicon germanium (SiGe) in the base of heterojunction bipolar transistors (HBTs) has recently put the alloy into prominence to produce fast-switching transistors. However, the thin highly doped SiGe base makes the transistor susceptible to base dopant outdiffusion during device processing, which results in device performance degradation. Adding carbon to the base was shown to significantly suppress boron outdiffusion and help retain the narrow as-grown profile. Dopant behavior in the presence of various species needs to be well understood and modeled for two reasons: (1) to have accurate and predictive process simulators; and (2) to obtain insight into process development. </p> <p> Modeling carbon diffusion and its role in suppressing boron diffusion in silicon and SiGe has been studied by several groups. While boron diffusion is well-established, different modeling regimes have been developed for carbon diffusion. Each of the existing studies has focused on subsets of the available experimental data. We present a consistent and complete model that accounts for carbon and boron diffusion in silicon and SiGe, under equilibrium and non-equilibrium conditions. In our regime, carbon diffusion is modeled according to the kick-out and Frank-Tumbull mechanisms for diffusion; in addition, we incorporate the carbon clustering phenomenon. To completely model boron diffusion, we account for the boron-interstitial clustering (BICs) effect and the { 311} defects that are associated with boron transient enhanced diffusion (TED). In the developed model we make use of the well-established literature data for carbon diffusion, as well as boron diffusion and Si self-diffusion. The model was verified by simulating experiments that involve boron and/or carbon diffusion in silicon and SiGe and cover the complete temperature range of 750 - 1070 °C. The test structures include published experiments in addition to recent experimental results obtained through collaboration, and feature diffusion in inert and oxidizing ambients, under rapid thermal annealing (RTA) conditions, as well as in the presence of implant damage. We also investigated the validation of the model without the inclusion of either the clustering or the Frank-Turnbull reactions. </p> / Thesis / Master of Applied Science (MASc)

The binding of the elements lithium and boron to human plasma proteins is investigated through the techniques of thermal neutron activation and helium isotope mass spectrometry. Since normal physiological levels of lithium and boron in blood and plasma are in the ultratrace (ppb) range, lithium in particular is frequently below the detection limit of many instruments. The success of the detection method used in this work is due to the extremely large cross section of ⁶Li for the thermal neutron reaction ⁶Li(n,³H)⁴He, and of ¹⁰B for the thermal neutron reaction ¹⁰B(n,α)⁷Li. The high sensitivity of the mass spectrometer, originally designed for oceanographic studies of helium isotopes and tritium, allows measurement of as little as 2x10⁴ atoms of ³He from the decay of tritium, and 2x10¹⁰ atoms of ⁴He from alpha-particles. It has frequently been stated that lithium does not bind to plasma proteins. However, our results clearly show that lithium does bind to a number of these proteins, at least 𝘪𝘯 𝘷𝘪𝘵𝘳𝘰. Boron is also shown to bind to proteins, with a pattern similar to that of lithium . Although a clear identification of the specific plasma proteins which bind lithium and boron must await further investigation, a number of possibilities are suggested here, based on the data obtained. / Thesis / Master of Science (MSc)

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yes / Boron is a vital element for growth of creations, but excessive exposure can cause detrimental effects to plants, animals, and possibly humans. Reverse Osmosis (RO) technique is widely used for seawater desalination as well as for waste water treatment. The aim of this study is to identify how different operating parameters such as pH, temperature and pressure can affect boron concentrations at the end of RO processes. For this purpose, a mathematical model for boron rejection is developed based on solution-diffusion model which can describe solvent and solute transport mechanism through the membranes. After a wide and thorough research, empirical correlations developed in the past are filtered, adopted and calibrated in order to faction with reliability as part of the solution-diffusion model of this work. The model is validated against a number of experimental results from the literature and is used in further simulations to get a deeper insight of the RO process. The general findings of the boron rejection model are supporting the case that with increasing pH and operating pressure of the feed water, the boron rejection increases and with increasing feed water temperature the boron rejection decreases.