N‐type doping of GaAs nanowires has proven to be difficult because the amphoteric character of silicon impurities is enhanced by the nanowire growth mechanism and growth conditions. The controllable growth of n‐type GaAs nanowires with carrier density as high as 1020 electron cm−3 by self‐assisted molecular beam epitaxy using Te donors is demonstrated here. Carrier density and electron mobility of highly doped nanowires are extracted through a combination of transport measurement and Kelvin probe force microscopy analysis in single‐wire field‐effect devices. Low‐temperature photoluminescence is used to characterize the Te‐doped nanowires over several orders of magnitude of the impurity concentration. The combined use of those techniques allows the precise definition of the growth conditions required for effective Te incorporation.

In this paper, we showcase the innovative concept of implementing Oscillatory Neural Networks (ONNs) for neuromorphic computing with beyond-CMOS devices based on vanadium dioxide to mimic neurons and resistors to emulate synapses. We explore ONN technology potentials from device to analog circuit-level simulations. We report that ONN behaves like an associative memory and can implement energy-based models such as Hopfield Neural Networks on edge devices. Finally, as a proof of concept, a reconfigurable digital ONN is implemented on FPGA for pattern recognition tasks.

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