Abstract: The magnetic flux domains in the intermediate state of type-I superconductors are known to resemble fluid droplets, and their dynamics in applied electric current is often cartooned as a “dripping faucet”. Here we show, using the time-depended Ginzburg-Landau simulations, that microfluidic principles hold also for the determination of the size of the magnetic flux-droplet as a function of the applied current, as well as for the merger or splitting of those droplets in the presence of the nanoengineered obstacles for droplet motion. Differently from fluids, the flux-droplets in superconductors are quantized and dissipative objects, and their pinning/depinning, nucleation, and splitting occur in a discretized form, all traceable in the voltage measured across the sample. At larger applied currents, we demonstrate how obstacles can cause branching of laminar flux streams or their transformation into mobile droplets, as readily observed in experiments.

Abstract: We show that a carbon nanotube decorated with different types of charged metallic nanoparticles exhibits unusual two-dimensional vibrations when actuated by applied electric field. Such vibrations and diverse possible trajectories are not only fundamentally important but also have minimum two characteristic frequencies that can be directly linked back to the properties of the constituents in the considered nanoresonator. Namely, those frequencies and the maximal deflection during vibrations are very distinctively dependent on the geometry of the nanotube, the shape, element, mass and charge of the nanoparticle, and are vastly tunable by the applied electric field, revealing the unique sensing ability of devices made of molecular filaments and metallic nanoparticles.

Abstract: <script type='text/javascript'>document.write(unpmarked('Surface-plasmon-polariton waves propagating at the interface between a metal and a dielectric, hold the key to future high-bandwidth, dense on-chip integrated logic circuits overcoming the diffraction limitation of photonics. While recent advances in plasmonic logic have witnessed the demonstration of basic and universal logic gates, these CMOS oriented digital logic gates cannot fully utilize the expressive power of this novel technology. Here, we aim at unraveling the true potential of plasmonics by exploiting an enhanced native functionality – the majority voter. Contrary to the state-of-the-art plasmonic logic devices, we use the phase of the wave instead of the intensity as the state or computational variable. We propose and demonstrate, via numerical simulations, a comprehensive scheme for building a nanoscale cascadable plasmonic majority logic gate along with a novel referencing scheme that can directly translate the information encoded in the amplitude and phase of the wave into electric field intensity at the output. Our MIM-based 3-input majority gate displays a highly improved overall area of only 0.636 mu m(2) for a single-stage compared with previous works on plasmonic logic. The proposed device demonstrates non-Boolean computational capability and can find direct utility in highly parallel real-time signal processing applications like pattern recognition.'));

Abstract: In this contribution, we report a fundamental study of the factors that set the contact resistivity between metals and highly doped semiconductors. We investigate the case of n-type doped Si contacted with amorphous TiSi combining first-principles calculations with Non-Equilibrium Green functions transport simulations. The intrinsic contact resistivity is found to saturate at similar to 2x10(-10) Omega.cm(2) with the doping concentration and sets an intrinsic limit to the ultimate contact resistance achievable for n-doped Si vertical bar amorphous-TiSi. This limit arises from the intrinsic properties of the semiconductor and of the metal such as their electron effective masses and Fermi energies. We illustrate that, in this regime, contacting metals with a heavy electron effective mass helps reducing the interface intrinsic contact resistivity.

Abstract: Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver.