  With remarkable electrical and optical switching properties induced at low
power and near room temperature (68C), vanadium dioxide (VO2) has sparked
rising interest in unconventional computing among the phase-change materials
research community. The scalability and the potential to compute beyond the von
Neumann model make VO2 especially appealing for implementation in oscillating
neural networks for artificial intelligence (AI) applications, to solve
constraint satisfaction problems, and for pattern recognition. Its integration
into large networks of oscillators on a Silicon platform still poses challenges
associated with the stabilization in the correct oxidation state and the
ability to fabricate a structure with predictable electrical behavior showing
very low variability. In this work, the role played by the different annealing
parameters applied by three methods (slow thermal annealing, flash annealing,
and rapid thermal annealing), following the vanadium oxide atomic layer
deposition (ALD), on the formation of VO2 grains is studied and an optimal
substrate stack configuration that minimizes variability between devices is
proposed. Material and electrical characterizations are performed on the
different films and a step-by-step recipe to build reproducible VO2-based
oscillators is presented, which is argued to be made possible thanks to the
introduction of a hafnium oxide (HfO2) layer between the silicon substrate and
the vanadium oxide layer. Up to seven nearly identical VO2-based devices are
contacted simultaneously to create a network of oscillators, paving the way for
large-scale implementation of VO2 oscillating neural networks.
