Direct and integrating sampling in terahertz receivers from wafer-scalable InAs nanowires.

Terahertz (THz) radiation will play a pivotal role in wireless communications, sensing, spectroscopy and imaging technologies in the decades to come. THz emitters and receivers should thus be simplified in their design and miniaturized to become a commodity. In this work we demonstrate scalable photoconductive THz receivers based on horizontally-grown InAs nanowires (NWs) embedded in a bow-tie antenna that work at room temperature. The NWs provide a short photoconductivity lifetime while conserving high electron mobility. The large surface-to-volume ratio also ensures low dark current and thus low thermal noise, compared to narrow-bandgap bulk devices. By engineering the NW morphology, the NWs exhibit greatly different photoconductivity lifetimes, enabling the receivers to detect THz photons via both direct and integrating sampling modes. The broadband NW receivers are compatible with gating lasers across the entire range of telecom wavelengths (1.2-1.6 µm) and thus are ideal for inexpensive all-optical fibre-based THz time-domain spectroscopy and imaging systems. The devices are deterministically positioned by lithography and thus scalable to the wafer scale, opening the path for a new generation of commercial THz receivers.

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.