Daniel John Mannion

We present a resistance switching device that exhibits analogue potentiation and depression of conductance under the same voltage polarity. This contrasts with previously studied devices that potentiate and depress under opposite polarities. We refer to this mode of operation as the subthreshold regime due to it occurring at voltage or current biases that are insufficient to produce discrete or non-volatile switching. This behaviour has the potential to reduce the complexity of neuronal and synaptic circuitry in neuromorphic computing by removing the need for voltage pulses of both positive and negative polarities. The characteristically long timescales may also help replicate bio-realistic timings. In this paper, we detail how to induce this unique behaviour, how to tune its properties to a desired response, and finally, we demonstrate one potential application.

The limited sensitivity of existing analysis techniques at the nanometer scale makes it challenging to systematically examine the complex interactions in redox-based resistive random access memory (ReRAM) devices. To test models of oxygen movement in ReRAM devices beyond what has previously been possible, we present a new nanoscale analysis method. Harnessing the power of secondary ion mass spectrometry, the most sensitive surface analysis technique, for the first time, we observe the movement of ¹⁶O across electrically biased SiO_x ReRAM stacks. We can therefore measure bulk concentration changes in a continuous profile with unprecedented sensitivity. This reveals the nanoscale details of the reversible field-driven exchange of oxygen across the ReRAM stack. Both the reservoir-like behavior of a Mo electrode and the injection of oxygen into the surface of SiO_x from the ambient are observed within one profile. The injection of oxygen is controllable through changing the porosity of the SiO_x layer. Modeling of the electric fields in the ReRAM stacks is carried out which, for the first time, uses real measurements of both the interface roughness and electrode porosity. This supports our findings helping to explain how and where oxygen from ambient moisture enters devices during operation.

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviours, the memristor has proved a versatile component. Here we demonstrate an analogue mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analogue behaviour can prove a scalable solution to edge detection. We confirm its behaviour experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.