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In this paper, we study the effect of phonon scattering in silicon nanowire field effect transistors (NWFET) using a Non-equilibrium Green’s function formalism in the effective mass approximation. The effect of electron-phonon scattering on the current voltage characteristics at high and low drain bias is investigated in detail. A wide range of cross-sections (from 2.2 × 2.2 to 6.2 × 6.2 nm2) and channel lengths (from 6 to 40 nm) are considered. The impact of phonon scattering on the electron current in different regions of the device characteristics is studied. Simulations including scattering in the whole transistor are compared with corresponding simulations in which scattering is only in the channel. Phonon limited mobility dependence on the NWFET cross-section and channel length is studied. The ballisticity coefficient, as a function of the channel length and gate voltage, is also computed for various channel cross-sections and lengths at high drain bias. The paper demonstrates that tunneling plays an important role in understanding the effect of phonon scattering at short channel lengths.

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This paper presents the development of a new exchange-correlation functional from the point of view of machine learning. Using atomization energies of solids and small molecules, we train a linear model for the exchange enhancement factor using a Bayesian approach which allows for the quantification of uncertainties in the predictions. A relevance vector machine is used to automatically select the most relevant terms of the model. We then test this model on atomization energies and also on bulk properties. The average model provides a mean absolute error of only 0.116 eV for the test points of the G2/97 set but a larger 0.314 eV for the test solids. In terms of bulk properties, the prediction for transition metals and monovalent semiconductors has a very low test error. However, as expected, predictions for types of materials not represented in the training set such as ionic solids show much larger errors.