Highly Reproducible and CMOS-compatible VO₂-based Oscillators for Brain-inspired Computing

Abstract

With remarkable electrical and optical switching properties induced at low power and near room temperature (68 ˚C), vanadium dioxide $(VO_2)$ has sparked rising interest in unconventional computing among the phase-change materials research community. [1] The scalability and the potential to compute beyond the von Neumann model make $VO_2$ especially appealing for implementation in oscillating neural networks for artificial intelligence (AI) applications, to solve constraint satisfaction problems, and for pattern recognition. [2, 3, 4, 5] 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. [6, 7] 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 $VO_2$ 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 $VO_2$-based oscillators is presented, which is argued to be made possible thanks to the introduction of a hafnium oxide $(HfO_2)$ layer between the silicon substrate and the vanadium oxide layer. Up to seven nearly identical $VO_2$-based devices are contacted simultaneously to create a network of oscillators, paving the way for large-scale implementation of $VO_2$ oscillating neural networks.