Publications

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Using first-principles density functional theory (DFT) methods and size-converged supercell models, we analyze the electronic and atomic structure of magnetic $3d$ transition metal dopants in cubic gallium nitride (c-GaN). All stable defect charge states for Fermi levels across the full experimental gap are computed using a method that correctly resolves the boundary condition problem (without a jellium approximation) and eliminates finite-size errors. The resulting computed defect levels are not impacted by the DFT band-gap problem, they span a width consistent with the experimental gap rather than being limited to the single-particle DFT gap. All defects with electronically degenerate (half-metal) $T$_d ground states are found to have significant distortions, relaxing to $D$_2d structures driven by the Jahn-Teller instability. This leads to insulating ground states for all substitutional $3d$ dopants, refuting claims in the literature that +$U$ or hybrid functional methods are required to avoid artificial half-metal results. Interpreting the $d$ⁿ atomic occupations within a crystal-field model and exchange splittings, we identify a systematic trend across the $3d$ transition metal series. Approaches to estimate excited-state energies as observed in photoluminescence from defect centers are assessed, ranging from a Koopmans-type single-particle energy interpretation to relaxed total energy differences in fully self-consistent DFT. The single-particle interpretations are found to be qualitatively predictive and the calculations are consistent with the limited available experimental data across the $3$d dopant series. These results provide a baseline understanding to guide future studies and a conceptual framework within which to interpret new results.

The E3 transition in irradiated GaAs observed in deep level transient spectroscopy (DLTS) was recently discovered in Laplace-DLTS to encompass three distinct components. The component designated E3c was found to be metastable, reversibly bleached under minority carrier (hole) injection, with an introduction rate dependent upon Si doping density. It is shown through first-principles modeling that the E3c must be the intimate Si-vacancy pair, best described as a Si sitting in a divacancy Si_vv. The bleached metastable state is enabled by a doubly site-shifting mechanism: Upon recharging, the defect undergoes a second site shift rather returning to its original E3c-active configuration via reversing the first site shift. Identification of this defect offers insights into the short-time annealing kinetics in irradiated GaAs.

Structural disorder causes materials' surface electronic properties, e.g., work function (φ), to vary spatially, yet it is challenging to prove exact causal relationships to underlying ensemble disorder, e.g., roughness or granularity. For polycrystalline Pt, nanoscale resolution photoemission threshold mapping reveals a spatially varying φ = 5.70 ± 0.03 eV over a distribution of (111) vicinal grain surfaces prepared by sputter deposition and annealing. With regard to field emission and related phenomena, e.g., vacuum arc initiation, a salient feature of the φ distribution is that it is skewed with a long tail to values down to 5.4 eV, i.e., far below the mean, which is exponentially impactful to field emission via the Fowler-Nordheim relation. We show that the φ spatial variation and distribution can be explained by ensemble variations of granular tilts and surface slopes via a Smoluchowski smoothing model wherein local φ variations result from spatially varying densities of electric dipole moments, intrinsic to atomic steps, that locally modify φ. Atomic step-terrace structure is confirmed with scanning tunneling microscopy (STM) at several locations on our surfaces, and prior works showed STM evidence for atomic step dipoles at various metal surfaces. From our model, we find an atomic step edge dipole μ = 0.12 D/edge atom, which is comparable to values reported in studies that utilized other methods and materials. Our results elucidate a connection between macroscopic φ and the nanostructure that may contribute to the spread of reported φ for Pt and other surfaces and may be useful toward more complete descriptions of polycrystalline metals in the models of field emission and other related vacuum electronics phenomena, e.g., arc initiation.

The stability of low-index platinum surfaces and their electronic properties is investigated with density functional theory, toward the goal of understanding the surface structure and electron emission, and identifying precursors to electrical breakdown, on nonideal platinum surfaces. Propensity for electron emission can be related to a local work function, which, in turn, is intimately dependent on the local surface structure. The (1×N) missing row reconstruction of the Pt(110) surface is systematically examined. The (1×3) missing row reconstruction is found to be the lowest in energy, with the (1×2) and (1×4) slightly less stable. In the limit of large (1×N) with wider (111) nanoterraces, the energy accurately approaches the asymptotic limit of the infinite Pt(111) surface. This suggests a local energetic stability of narrow (111) nanoterraces on free Pt surfaces that could be a common structural feature in the complex surface morphologies, leading to work functions consistent with those on thermally grown Pt substrates.

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This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

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