Silicon nanowire arrays as learning chemical vapour classifiers.

Nanowire field-effect transistors are a promising class of devices for various sensing applications. Apart from detecting individual chemical or biological analytes, it is especially interesting to use multiple selective sensors to look at their collective response in order to perform classification into predetermined categories. We show that non-functionalised silicon nanowire arrays can be used to robustly classify different chemical vapours using simple statistical machine learning methods. We were able to distinguish between acetone, ethanol and water with 100% accuracy while methanol, ethanol and 2-propanol were classified with 96% accuracy in ambient conditions.

Shot-noise-driven escape in hysteretic Josephson junctions.

We have measured the influence of shot noise on hysteretic Josephson junctions initially in the macroscopic quantum tunneling regime. The escape threshold current into the resistive state decreases monotonically with increasing average current through the scattering conductor, which is another tunnel junction. Escape is predominantly determined by excitation due to the wideband shot noise. This process is equivalent to thermal activation (TA) over the barrier at effective temperatures up to about 4 times the critical temperature of the superconductor. The presented TA model is in excellent agreement with the experimental results.