Resistive Switching in Silicon-rich Silicon Oxide

Over the recent decade, many different concepts of new emerging memories have been proposed. Examples of such include ferroelectric random access memories (FeRAMs), phase-change RAMs (PRAMs), resistive RAMs (RRAMs), magnetic RAMs (MRAMs), nano-crystal floating-gate flash memories, among others. The ultimate goal for any of these memories is to overcome the limitations of dynamic random access memories (DRAM) and flash memories. Non-volatile memories exploiting resistive switching – resistive RAM (RRAM) devices – offer the possibility of low programming energy per bit, rapid switching, and very high levels of integration – potentially in 3D. Resistive switching in a silicon-based material offers a compelling alternative to existing metal oxide-based devices, both in terms of ease of fabrication, but also in enhanced device performance. In this thesis I demonstrate a redox-based resistive switch exploiting the formation of conductive filaments in a bulk silicon-rich silicon oxide. My devices exhibit multi-level switching and analogue modulation of resistance as well as standard two-level switching. I demonstrate different operational modes (bipolar and unipolar switching modes) that make it possible to dynamically adjust device properties, in particular two highly desirable properties: non-linearity and self-rectification. Scanning tunnelling microscopy (STM), atomic force microscopy (AFM), and conductive atomic force microscopy (C-AFM) measurements provide a more detailed insight into both the location and the dimensions of the conductive filaments. I discuss aspects of conduction and switching mechanisms and we propose a physical model of resistive switching. I demonstrate room temperature quantisation of conductance in silicon oxide resistive switches, implying ballistic transport of electrons through a quantum constriction, associated with an individual silicon filament in the SiOx bulk. I develop a stochastic method to simulate microscopic formation and rupture of conductive filaments inside an oxide matrix. I use the model to discuss switching properties – endurance and switching uniformity

New Paradigm Technology

The rapid development of computing technology is reflected in the fact that industry has consistently doubled the number of transistors per unit area on a semiconductor wafer every two years. Essentially the basic business model of the semiconductor industry, processor and memory technology has so far continued to roughly fulfil this doubling convention, despite technological barriers and fluctuating economic conditions. Many other aspects of computing technology have followed similar exponential laws, including hard drive space and internet connection speeds. However, the expiry of such rapid development has been forecast on multiple occasions as technological hurdles become increasingly more challenging

Brain-inspired computing needs a master plan

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing

badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays

Crossbar arrays are a popular solution when implementing systems that have array-like architecture. With the recent developments in the field of neuromorphic engineering, crossbars are now routinely used to implement artificial neural networks or, more generally, to perform vector–matrix multiplication in hardware. However, the interconnect resistance present in all crossbars can lead to significant deviations from the intended behaviour of these structures. In this work, we present badcrossbar—an open-source tool for computing currents and voltages in such non-ideal passive crossbar arrays. Additionally, the package allows to easily visualise currents and voltages (or other numerical variables) in the branches and on the nodes of these structures

Light-activated resistance switching in SiOx RRAM devices

We report a study of light-activated resistance switching in silicon oxide (SiOx) resistive random access memory (RRAM) devices. Our devices had an indium tin oxide/SiOx/p-Si Metal/Oxide/ Semiconductor structure, with resistance switching taking place in a 35 nm thick SiOx layer. The optical activity of the devices was investigated by characterising them in a range of voltage and light conditions. Devices respond to illumination at wavelengths in the range of 410–650 nm but are unresponsive at 1152 nm, suggesting that photons are absorbed by the bottom p-type silicon electrode and that generation of free carriers underpins optical activity. Applied light causes charging of devices in the high resistance state (HRS), photocurrent in the low resistance state (LRS), and lowering of the set voltage (required to go from the HRS to LRS) and can be used in conjunction with a voltage bias to trigger switching from the HRS to the LRS. We demonstrate negative correlation between set voltage and applied laser power using a 632.8 nm laser source. We propose that, under illumination, increased electron injection and hence a higher rate of creation of Frenkel pairs in the oxide—precursors for the formation of conductive oxygen vacancy filaments—reduce switching voltages. Our results open up the possibility of light-triggered RRAM devices

The role of physics in epithelial homeostasis and development

Developing epithelial tissues are characterised by the disordered cell packing caused by ongoing cell proliferation and changes in tissue size. However, cell packing in adult epithelial tissues exhibits a high level of order, and typically, the apical tissue surface resembles a regular hexagonal lattice of planar polygons. One of the central questions in tissue development concerns the mechanisms which induce cells to repack. The change in packing may transform the tissue into a regular pattern of hexagonal cells, as seen during the refinement of Drosophila M. wing and notum tissue, or it can occur as a mechanism which drives tissue shape change, as seen during embryonal axis elongation during Drosophila convergent extension. We study cell repacking in epithelia effected by the forces that act at the interface between adjacent cells. To this end, we develop a mechanical model of epithelial tissue based on the ideas of the cellular Potts model and building on previous vertex models. Analysing expanding and fixed-size tissues, we find that steady state packing geometries depend on the regularity in the timing of cell divisions. We predict that cells in topologically active epithelia leave the tissue in response to mechanical compression and geometric anisotropy. Through a collaboration with biologists Eliana Marinari and Buzz Baum, we find that such mechanically driven cell delamination indeed occurs in the Drosophila notum. We thus identify a novel process of tissue homeostasis, whereby live cells delaminate from developing epithelium in order to limit overcrowding. Analysing the relation between stable packing geometries and the mechanical parameters, we suggest that an increase in the strength of acto-myosin contractility alone could cause tissue to repack into a regular lattice. Modifying the model to describe polarised acto-myosin localisation, we computationally reproduce cell intercalation and actin cable and rosette formation during convergent extension in Drosophila

Multi-channel conduction in redox-based resistive switch modelled using quantum point contact theory

A simple analytic model for the electron transport through filamentary-type structures in Si-rich silica (SiOx)-based resistive switches is proposed. The model is based on a mesoscopic description and is able to account for the linear and nonlinear components of conductance that arise from both fully and partially formed conductive channels spanning the dielectric film. Channels are represented by arrays of identical scatterers whose number and quantum transmission properties determine the current magnitude in the low and high resistance states. We show that the proposed model not only reproduces the experimental current-voltage (I-V) characteristics but also the normalized differential conductance (dln(I)/dln(V)-V) curves of devices under test

Memristor-Based Edge Detection for Spike Encoded Pixels

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog 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 analog behavior can prove a scalable solution to edge detection. We confirm its behavior 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

High Performance Resistance Switching Memory Devices Using Spin-on Silicon Oxide

In this paper, we present high performance resistance switching memory devices (RRAM) with an SiO 2 -like active layer formed from spin-on hydrogen silsesquioxane (HSQ). Our metal-insulator-metal (MIM) devices exhibit switching voltages of less than 1 V, cycling endurances of more than 10 7 cycles without failure, electroforming below 2 V and retention time of resistance states of more than 10 5 seconds at room temperature. We also report arrays of nanoscale HSQ-based RRAM devices in the form of multilayer nanopillars with switching performance comparable to that of our thin film devices. We are able to address and program individual RRAM nanopillars using conductive atomic force microscopy. These promising results, coupled with a much easier fabrication method than traditional ultra-high vacuum based deposition techniques, make HSQ a strong candidate material for the next generation memory devices