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Trap-Assisted Auger-Meitner Recombination from First Principles

Trap-assisted nonradiative recombination is known to limit the efficiency of optoelectronic devices, but the conventional multiphonon emission (MPE) process fails to explain the observed loss in wide-band-gap materials. Here, we highlight the role of trap-assisted Auger-Meitner (TAAM) recombination and present a first-principles methodology to determine TAAM rates due to defects or impurities in semiconductors or insulators. We assess the impact on efficiency of light emitters in a recombination cycle that may include both TAAM and carrier capture via MPE. We apply the formalism to the technologically relevant case study of a calcium impurity in InGaN, where a Shockley-Read-Hall recombination cycle involving MPE alone cannot explain the experimentally observed nonradiative loss. We find that, for band gaps larger than 2.5 eV, the inclusion of TAAM results in recombination rates that are orders of magnitude larger than recombination rates based on MPE alone, demonstrating that TAAM can be a dominant nonradiative process in wide-band-gap materials. Furthermore, our computational formalism is general and can be applied to the calculation of TAAM rates in any semiconducting or insulating material.

36 MATERIALS SCIENCE↗

Carbon in GaN as a nonradiative recombination center

Trap-assisted nonradiative recombination has been shown to limit the efficiency of optoelectronic devices. While substitutional carbon (⁠C N ) has been suggested to be a nonradiative recombination center in GaN devices, a complete recombination cycle including the two charge-state transition levels has not been previously described. In this work, we investigate the trap-assisted recombination process due to C N in GaN, including multiphonon emission, radiative recombination, trap-assisted Auger–Meitner (TAAM) recombination as well as thermal emission of holes. Our study shows the key role of TAAM processes at the high carrier densities relevant for devices. We also reveal the carrier-density regimes where thermal emission and radiative recombination are expected to play an observable role. Our results highlight that carbon concentrations exceeding ~10 17 cm −3 can have a noticeable impact on device efficiency, not just in GaN active layers but also in InGaN and AlGaN. Furthermore, our comprehensive formalism not only offers detailed results for carbon but also provides a general framework for assessing the multiple processes that participate in trap-assisted recombination in semiconductors.

36 MATERIALS SCIENCE↗

Transferable Hirshfeld atom model for rapid evaluation of aspherical atomic form factors

Form factors based on aspherical models of atomic electron density have brought great improvement in the accuracies of hydrogen atom parameters derived from X-ray crystal structure refinement. Today, two main groups of such models are available, the banks of transferable atomic densities parametrized using the Hansen–Coppens multipole model which allows for rapid evaluation of atomic form factors and Hirshfeld atom refinement (HAR)-related methods which are usually more accurate but also slower. In this work, a model that combines the ideas utilized in the two approaches is tested. It uses atomic electron densities based on Hirshfeld partitions of electron densities, which are precalculated and stored in a databank. This model was also applied during the refinement of the structures of five small molecules. A comparison of the resulting hydrogen atom parameters with those derived from neutron diffraction data indicates that they are more accurate than those obtained with the Hansen–Coppens based databank, and only slightly less accurate than those obtained with a version of HAR that neglects the crystal environment. The advantage of using HAR becomes more noticeable when the effects of the environment are included. To speed up calculations, atomic densities were represented by multipole expansion with spherical harmonics up to l = 7, which used numerical radial functions (a different approach to that applied in the Hansen–Coppens model). Calculations of atomic form factors for the small protein crambin (at 0.73 Å resolution) took only 68 s using 12 CPU cores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗