Understanding the Microscopic Mechanisms of Auger Recombination in Crystalline Silicon

Auger recombination is an intrinsic, non-radiative recombination mechanism involving three carriers – either two electrons and a hole (eeh) or two holes and an electron (hhe). Despite silicon’s overwhelming importance as a semiconductor, the microscopic mechanisms of Auger recombination in silicon remain poorly understood. In this work, we use first principles methods to probe both direct Auger, where momentum is strictly conserved by the recombining electrons and holes, as well as indirect (phonon-assisted) Auger, which is enabled by the additional momentum provided via electron-phonon coupling. In addition to assessing the relative strength of the different Auger mechanisms, we also investigate the contribution of different electronic valleys and phonon modes to the overall Auger recombination rate. We also examine the effects of temperature and carrier density in the high-density regime on the Auger rates. The result of these efforts is a hitherto inaccessible understanding of Auger recombination at an atomic scale.<br/> <br/>We demonstrate that phonon-assisted Auger is the dominant mechanism for both the eeh and hhe processes. Our results are in excellent agreement with experimental measurements. Furthermore, our analysis of the valley contributions to Auger reveals that that for eeh Auger, the strongest recombination occurs from electrons occupying valleys that are perpendicular to one another. This finding indicates that it may be possible to tune the Auger recombination rate in silicon via strain engineering – a boon given that Auger is an intrinsic recombination process. Furthermore, we have found that the hhe process is dominated by short wavelength phonons, while phonons across the wavelength spectrum all contribute to the eeh process. Ultimately, our computational characterization paves the way for a clearer understanding of the microscopic Auger recombination mechanisms in silicon, and points to engineering solutions that may improve the efficiency of silicon solar cells.<br/> <br/>This work was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award No. DE-SC0020129. Computational resources were provided by the National Energy Research Scientific Computing (NERSC) Center, a DOE Office of Science User Facility supported under Contract No. DE-AC02–05CH11231. K.B. acknowledges the support of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0020347.