title: V3O5 as a Functional Material for a Solid-State Neuron
Creators: name: Das, Sujan Kumar
Publisher: name: The Australian National University
Year: 2025

description: Artificial intelligence (AI) is evident in every facet of our lives but traditional computing, based on CMOS technology and von Neumann architecture, is very inefficient for such tasks. Neuromorphic computing aims to address this shortcoming by emulating the computational architecture of the brain. This typically takes the form of a spiking neural network (SNN) comprising a highly interconnected network of solid-state synapses and neurons, where the synapses serve as distributed memory (synaptic weights) and the neurons function as simple processing units. This thesis is focused on the development of a suitable solid-state neuron based on threshold switching in V3O5. In the first part of this thesis, the structural, electrical, and thermal properties of V3O5 thin films are investigated. X-ray diffraction (XRD), Raman spectroscopy, and electron diffraction techniques show that the as-deposited V3O5 films are polycrystalline with a monoclinic structure. Electrical characterisation shows that the films undergo an insulator-metal transition (IMT) at 420 K. The IMT occurs over a broad temperature range due to the inhomogeneous nature of the V3O5 phase transition, as indicated by temperature-dependent C-AFM maps of through-film current distributions. The thermal conductivity of the V3O5 films was shown to increase slightly with temperature below the IMT temperature and significantly during the IMT due to the increasing electronic contribution. These properties show that V3O5 holds promise as a functional oxide for threshold switching applications. The second part of this thesis focuses on the threshold switching characteristics of Pt/V3O5/Pt memristors. These devices exhibit volatile threshold switching and negative differential resistance (NDR) from the first voltage-sweep and stable cycle-to-cycle operation, with a variation in switching parameters of less than 3 percent during 10000 cycles. The threshold and hold voltages decrease with increasing substrate temperature while still maintaining an NDR window up to the V3O5 IMT temperature. The physical mechanisms underpinning these characteristics are investigated using a combination of electrical measurements, in situ thermal imaging, and device modelling. This shows that conduction is confined to a narrow filamentary path due to the self-confinement of the current distribution. Additionally, it shows that the threshold switching and NDR response are initiated at temperatures well below the IMT temperature due to the temperature-dependent conductivity of the insulating phase. Variations and discontinuities in the NDR slope are further shown to be consistent with the averaging of the electrical oscillations. In the final part, the application of V3O5 memristors as solid-state neurons is investigated. This focuses on the dynamics of individual and coupled relaxation oscillators and leaky integrate-and-fire (LIF) neurons. It is observed that individual oscillators exhibit a fast-spiking pattern and maintain stable relaxation oscillation even at operating temperatures exceeding 400 K. Capacitively coupled oscillators are then shown to exhibit rich non-linear dynamics, including frequency and phase synchronisation, which is a key feature for oscillator-based computing. Effective LIF neuron's behaviour can be tuned through relative timing and amplitude of the external stimulus, and resistively coupled LIF neurons exhibit various neural functionalities (i.e. phasic, regular and adaptation, etc.) depending on the input voltage and circuit parameters. The behaviours of both individual and coupled neurons are shown to be well described by a physics-based lumped element circuit model, which, therefore, provides a solid foundation for exploring more complex systems. These results establish V3O5 as a potential functional material for volatile threshold switching and advance the development of robust solid-state neurons with low power consumption for neuromorphic computing.