In order to understand the influence of dislocations on doping and compensation in Al-rich AlGaN, thin films were grown by metal organic chemical vapor deposition (MOCVD) on different templates on sapphire and low dislocation density single crystalline AlN. AlGaN grown on AlN exhibited the highest conductivity, carrier concentration, and mobility for any doping concentration due to low threading dislocation related compensation and reduced self-compensation. The onset of self-compensation, i.e., the “knee behavior” in conductivity, was found to depend only on the chemical potential of silicon, strongly indicating the cation vacancy complex with Si as the source of self-compensation. However, the magnitude of self-compensation was found to increase with an increase in dislocation density, and consequently, AlGaN grown on AlN substrates demonstrated higher conductivity over the entire doping range.
In the low doping range below 1 × 1017 cm−3, carbon was identified as the main defect attributing to the sudden reduction of the electron mobility, the electron mobility collapse, in n-type GaN grown by low pressure metalorganic chemical vapor deposition. Secondary ion mass spectroscopy has been performed in conjunction with C concentration and the thermodynamic Ga supersaturation model. By controlling the ammonia flow rate, the input partial pressure of Ga precursor, and the diluent gas within the Ga supersaturation model, the C concentration in Si-doped GaN was controllable from 6 × 1019 cm−3 to values as low as 2 × 1015 cm−3. It was found that the electron mobility collapsed as a function of free carrier concentration, once the Si concentration closely approached the C concentration. Lowering the C concentration to the order of 1015 cm−3 by optimizing Ga supersaturation achieved controllable free carrier concentrations down to 5 × 1015 cm−3 with a peak electron mobility of 820 cm2/V s without observing the mobility collapse. The highest electron mobility of 1170 cm2/V s was obtained even in metalorganic vapor deposition-grown GaN on sapphire substrates by optimizing growth parameters in terms of Ga supersaturation to reduce the C concentration.
In this work, we employed X-ray photoelectron spectroscopy to determine the band offsets and interface Fermi level at the heterojunction formed by stoichiometric silicon nitride deposited on AlxGa1-xN (of varying Al composition “x”) via low pressure chemical vapor deposition. Silicon nitride is found to form a type II staggered band alignment with AlGaN for all Al compositions (0 ≤ x ≤ 1) and present an electron barrier into AlGaN even at higher Al compositions, where Eg(AlGaN) > Eg(Si3N4). Further, no band bending is observed in AlGaN for x ≤ 0.6 and a reduced band bending (by ∼1 eV in comparison to that at free surface) is observed for x > 0.6. The Fermi level in silicon nitride is found to be at 3 eV with respect to its valence band, which is likely due to silicon (≡Si0/−1) dangling bonds. The presence of band bending for x > 0.6 is seen as a likely consequence of Fermi level alignment at Si3N4/AlGaN hetero-interface and not due to interface states. Photoelectron spectroscopy results are corroborated by current-voltage-temperature and capacitance-voltage measurements. A shift in the interface Fermi level (before band bending at equilibrium) from the conduction band in Si3N4/n-GaN to the valence band in Si3N4/p-GaN is observed, which strongly indicates a reduction in mid-gap interface states. Hence, stoichiometric silicon nitride is found to be a feasible passivation and dielectric insulation material for AlGaN at any composition.
A theoretical framework that provides a quantitative relationship between point defect formation energies and growth process parameters is presented. It enables systematic point defect reduction by chemical potential control in metalorganic chemical vapor deposition (MOCVD) of III-nitrides. Experimental corroboration is provided by a case study of C incorporation in GaN. The theoretical model is shown to be successful in providing quantitative predictions of CN defect incorporation in GaN as a function of growth parameters and provides valuable insights into boundary phases and other impurity chemical reactions. The metal supersaturation is found to be the primary factor in determining the chemical potential of III/N and consequently incorporation or formation of point defects which involves exchange of III or N atoms with the reservoir. The framework is general and may be extended to other defect systems in (Al)GaN. The utility of equilibrium formalism typically employed in density functional theory in predicting defect incorporation in non-equilibrium and high temperature MOCVD growth is confirmed. Furthermore, the proposed theoretical framework may be used to determine optimal growth conditions to achieve minimum compensation within any given constraints such as growth rate, crystal quality, and other practical system limitations.
Reduction in compensation in Si-doped Al-rich AlGaN is demonstrated via chemical potential control (CPC). The chemical potentials and the resulting formation energies of carbon on the nitrogen site (CN) and cation vacancy complex with Si (VIII + nSiIII) were related to growth variables through a thermodynamic supersaturation model, which quantitatively predicted the incorporation of CN and the generation of the VIII + nSiIII complex. The compensation “knee” behavior, i.e., decreasing conductivity with increasing Si incorporation beyond a certain concentration, was successfully controlled. The maximum free carrier concentration was improved by impeding the formation of VIII + nSiIII complexes under III-richer conditions, while the impurity compensation by CN was reduced by making the growth environment N-richer. The results of Hall effect measurement and photoluminescence agreed well with quantitative theoretical predictions of the CPC model. Based on the developed model, the highest conductivity of 160 Ω−1 cm−1 with free carrier concentration of 3 × 1019 cm−3 in Al0.7Ga0.3N ever reported was achieved on single crystal AlN substrates. The demonstrated predictive power of the CPC model should greatly reduce the empirical analysis or iterative experimentation that would otherwise be necessary.
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