Electron transport in nanocomposites has attracted a good deal of attention for some time now; furthermore, the ability to control its characteristics is a necessary step in the design of multifunctional materials. When conductive nanostructures (for example carbon nanotubes) are inserted in a non-conductive matrix, electron transport below the percolation threshold is dominated by tunneling and thus the conductive characteristics of the composite depends heavily on the characteristics of the tunneling currents between nanoinserts. A parameter-free approach to study tunneling transport between carbon nanotubes across a polymer matrix is presented. The calculation is done with a combination of Density Functional Theory and Green functions (an approach heavily used in molecular electronics) which is shown here to be effective in this non-resonant transport condition. The results show that the method can effectively capture the effect of a dielectric layer in tunneling transport. The current is found to exponentially decrease with the size of the gap for both vacuum and polymer, and that the polymer layer lowers the tunneling barrier enhancing tunneling conduction. For a polyacrylonitrile matrix, a four-fold decrease in the tunneling constant, compared to tunneling in vacuum, is observed, a result that is consistent with available information. The method is very versatile as any DFT functional (or any other quantum mechanics method) can be used and thus the most accurate method for each particular system can be chosen. Furthermore as more methods become available, the calculations can be revised and improved. This approach can be used to design functional materials for fine-tunning the tunneling transport, for instance, the effect of modifying the nanoinsert-matrix interface (for example, by adding functional groups to carbon nanotubes) can be captured and the comparative performance of each interface predicted by simulation.
Millisecond anneal techniques have been demonstrated to achieve fully recrystallized, highly activated, shallow, and abrupt junctions in silicon with both p- and n-type dopants due to the technique's fast time scale and high temperature. To understand and model the effects of millisecond laser annealing, knowledge of the true thermal profile experienced by the active semiconductor region must be known. This work simulates the impacts of a scanning laser in a series of shallow implants, and compares those results to experimental results. Arsenic ion (As+) implant energies of 10, 19, and 25 keV at doses of 1.5 × 1015 and 3 × 1015 cm−2 into a silicon-on-insulator substrate are studied to achieve different doping levels and amorphization depths. The recrystallization, activation, and mobility of the laser annealed, ion implanted experimental cells are then analyzed. For each experiment, Sentaurus technology computer aided design is used to create a calibrated 2D laser model to approximate the thermal budget of the lasing recipes (850–1250 °C) then using that output as an input into lattice kinetic Monte Carlo (LKMC) to simulate the solid phase epitaxial regrowth (SPER) during anneal of the various implant conditions. Sheet resistance and Hall effect measurements were used to correlate dopant activation and mobility with the regrowth process during laser anneal, showing the onset of high conductivity associated with completion of SPER in the films. The LKMC model shows an excellent agreement with cross section transmission electron microscopy, correlating the increase of conductivity with completion of crystal regrowth, increased activation, and crystal quality at various temperatures. Shallow, lower dose implants are capable of single crystal regrowth, producing high levels of activation >1 × 1020 cm−2 and nominal mobilities for highly arsenic-doped silicon. However, higher energy implants that fully amorphize the film regrow polycrystalline silicon with low mobilities even at very high temperatures (1250 °C), unsuitable for source–drain formation in logic devices.
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