Dual-wavelength ultraviolet photodetector based on vertical(Al,Ga)N nanowires and graphene

Due to the wide application of UV-A(320 nm–400 nm)and UV-C(200 nm–280 nm)photodetectors,dual-wavelength(UV-A/UV-C)photodetectors are promising for future markets.A dual-wavelength UV photodetector based on vertical(Al,Ga)N nanowires and graphene has been demonstrated successfully,in which graphene is used as a transparent electrode.Both UV-A and UV-C responses can be clearly detected by the device,and the rejection ratio(R254 nm/R450 nm)exceeds35 times at an applied bias of-2 V.The short response time of the device is less than 20 ms.Furthermore,the underlying mechanism of double ultraviolet responses has also been analyzed systematically.The dual-wavelength detections could mainly result from the appropriate ratio of the thicknesses and the enough energy band difference of(Al,Ga)N and Ga N sections.

Realize ultralow-energy-consumption photo-synaptic device based on a single(Al,Ga)N nanowire for neuromorphic computing

The rapid development of artificial intelligence poses an urgent need for low-energy-consumption and small-sized artificial photonic synapses.Here,it is pretty novel to demonstrate a light-stimulated synaptic device based on a single(Al,Ga)N nanowire successfully.Thanks to the presence of vacancy defects in the single nanowire,the artificial synaptic device can simulate multiple functions of biological synapses under stimulation of both 310 and 365 nm light photons,including paired-pulse facilitation,spike timing dependent plasticity,and memory learning capabilities.The energy consumption of artificial synaptic device can be reduced as little as 5.58×10^(-13) J,which is close to that of the biological synapse in human brain.Furthermore,the synaptic device is demonstrated to have the high stability for both long-time stimulation and long-time storage.Based on the experimental conductance of long-term potentiation and long-term depression,the simulated three-layer neural network can achieve a high recognition rate of 92%after only 10 training epochs.With a brain-like behavior,the single-nanowire-based synaptic devices can promote the development of visual neuromorphic computing technology and artificial intelligence systems requiring ultralow energy consumption.