The effects of contact geometry and specific contact resistivity on In0.53Ga0.47As (InGaAs) and silicon (Si) nanoscale (18 nm channel length) n-channel FinFETs performance, and the effects of models thereof, are studied using a quantum-corrected semiclassical Monte Carlo method. Saddle/slot, raised source and drain (RSD), and reference end contacts are modeled. Both ideal perfectly injecting and absorbing contacts and those with more realistic specific contact resistivities are considered. Far-from-equilibrium degenerate statistics, quantum-confinement effects on carrier distributions in real-space and among energy valleys and on scattering, and quasiballistic transport are modeled. Silicon ⟨110⟩ channel and Si ⟨100⟩ channel FinFETs, multivalley InGaAs channel FinFETs with conventionally reported InGaAs energy valley offsets, and reference idealized Γ-valley-only InGaAs (Γ-InGaAs) channel FinFETs are simulated. Among our findings, InGaAs channel FinFETs are highly sensitive to modeled contact geometry and specific contact resistivity and to the band structure model, while Si channel FinFETs showed still significant but much less sensitivity to the contact models. For example, for idealized unity transmissivity contacts, Γ-InGaAs channel FinFETs performed best for all contact geometries, at least in terms of transconductance, and end contacts provided the best performance for all considered channel materials. For realistic contact resistivities, however, the results are essentially reversed. Silicon channel FinFETs performed best for all contact geometries, and saddle/slot and RSD contacts outperformed end contacts.

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Transition metal-oxide resistive random-access memories seem to be a viable candidate as the nextgeneration storage technology because transition metals have multiple oxidation states and are good ionic conductors. A wide range of transition metal oxides have recently been studied; however, fundamental understanding of the switching mechanism is still lacking. Migration energies and diffusivity of oxygen vacancies in amorphous and crystalline HfO 2 and CeO 2 and at their interface are investigated by employing density functional theory. We found that oxygen dynamics is better in CeO 2 compared to HfO 2 , including smaller activation energy barriers and larger diffusion pre-factors, which can have implications in the material-selection process to determine which combination of materials offer the most efficient switching. Furthermore, we found that motion of vacancies is different at the interface of these two oxides as compared to that within each constituents, which provided insight into the role of the interface in vacancy motion and ultimately using interface engineering as a way to tune material properties.

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