Memristor devices based on low-bandwidth manganites

This dissertation investigates the phenomenon of resistive switching (RS) in lowbandwidth mixed-valence perovskite manganite oxides. In particular, the compounds Pr0.6Ca0.4MnO3 and Gd1−xCaxMnO3 with x between 0 and 1 are studied. The steps of sample fabrication, crystalline properties and measurements to verify the quality of the devices are also reported. The thin film memristor devices were fabricated from target pellets using pulsed laser deposition on single crystal SrTiO3 substrates. The crystallinity was verified using X-ray diffraction and the elemental composition by energy dispersive X-ray spectroscopy. The fabricated thin films were used to create memristor devices by depositing patterned metal electrodes on them by either DC magnetron sputtering or e-beam physical vapor deposition. When the studied materials were combined with a reactive electrode material, the formed interface exhibited the phenomenon of resistive switching, where the resistance of the device can be modified non-volatilely by application of electric field to the terminals of the device. The noble metals Au and Ag were found to be optimal for the passive interfaces, and Al as the active interface. The RS properties of the devices made with the optimal electrode configuration were studied in detail. The devices were found to have asymmetric bipolar RS with promising characteristics. The studies encompassed varying the calcium doping of the samples, studying the endurance and timing characteristics of the RS phenomenon as well as measuring the device characteristics as a function of temperature. The RS properties were found to vary significantly over the calcium doping range. When the measurement results were used in a conduction model analysis, the switching properties were found to be correlated with the trap-energy level of the Al/GCMOinterface region. Lastly, the GCMO memristor devices were modeled successfully using a compact model compatible with circuit simulators and the biologicallyinspired spike-timing-dependent plasticity learning rule was demonstrated. In conclusion, GCMO is a promising new material for RS-based neuromorphic applications due to its stable switching properties. The unexpected differences between GCMO and PCMO show that there are still many unexplored RS properties and behaviors within the manganite family that can be explored in future research

Tuning resistive switching in complex oxide memristors

The continuous demand of lightweight portable, cheap and low-power devices has pushed the electronic industry to the limits of the current technology. Flash memory technology which represents the mainstream non-volatile memories has experienced an impressive development over the last decade. This led their fabrication down to a 16 nm node and implementation of high-density 3D memory architectures. Due to the scaling limit of Flash technology, the need of new memories that combine the characteristics of a Flash but overcome the scaling limits is increasing. In this surge, oxide-based resistive memories – also called memristors – have emerged as a new family of storage-class memory. The extremely simple physical structure fast response, low cost and power consumption render resistive memories as a valid alternative of the Flash technology and an optimal choice for the next generation memory technology. The nanoscale resistive memories have demonstrated a variety of memory characteristics which depends on the electrochemical properties of the oxide system and several physical parameters including device structure and electrical biasing conditions. This indicates a complex nature of the underlying microscopic switching mechanisms which require a thorough understanding in order to fully benefit from the virtue of this technology. The work presented in this Doctoral Dissertation focuses on the realization and fine tuning the memory characteristics of SrTiO3 based resistive switching memories. A novel synthesis route is adopted to realize highly complementary metal oxide semiconductor (CMOS) compatible nanoscale memristive devices and engineer the composition of the functional SrTiO3 perovskite oxide. By following the novel synthesis approach, SrTiO3 memristive devices with different stoichiometry such as different concentration of oxygen vacancies, metallic dopant species and physical structures are fabricated to achieve multifunctional characteristics of these devices. Rigorous electrical and material characterizations are carried out to analyze the resistive switching performance and understand the underlying microscopic mechanisms. Stable multi-state resistive switching is demonstrated in donor (Nb) doped oxygen-deficient amorphous SrTiO3 (Nb:a-STOx) memories. The dynamics of multi-state switching behavior and the effect of Nb-doping on tuning the resistive switching are investigated by utilizing a combination of interfacial compositional evaluation and activation energy measurements. Furthermore, multiple switching behaviors in a single acceptor (Cr) doped amorphous SrTiO3 (Cr:a-STOx) memory cell are demonstrated. A physical model is also suggested to explain the novel switching characteristics of these versatile memristive devices. A highly transparent and multifunctional SrTiO3 based memory system is fabricated which offers a reliable data storage and photosensitive platform for further transparent electronics. Also a unique photoluminescence mapping is presented as an identification technique for localized conduction mechanism in oxide resistive memories. Finally, SrTiO3 resistive memories are engineered to mimic biological synapses. A hybrid CMOS-memristor approached is presented to demonstrate first implementation of higher order time and rate dependent synaptic learning rules. Furthermore, these artificial synapses are tuned for energy-efficient performance to highlight their potential for the future neuromorphic networks

On the Application of PSpice for Localised Cloud Security

The work reported in this thesis commenced with a review of methods for creating random binary sequences for encoding data locally by the client before storing in the Cloud. The first method reviewed investigated evolutionary computing software which generated noise-producing functions from natural noise, a highly-speculative novel idea since noise is stochastic. Nevertheless, a function was created which generated noise to seed chaos oscillators which produced random binary sequences and this research led to a circuit-based one-time pad key chaos encoder for encrypting data. Circuit-based delay chaos oscillators, initialised with sampled electronic noise, were simulated in a linear circuit simulator called PSpice. Many simulation problems were encountered because of the nonlinear nature of chaos but were solved by creating new simulation parts, tools and simulation paradigms. Simulation data from a range of chaos sources was exported and analysed using Lyapunov analysis and identified two sources which produced one-time pad sequences with maximum entropy. This led to an encoding system which generated unlimited, infinitely-long period, unique random one-time pad encryption keys for plaintext data length matching. The keys were studied for maximum entropy and passed a suite of stringent internationally-accepted statistical tests for randomness. A prototype containing two delay chaos sources initialised by electronic noise was produced on a double-sided printed circuit board and produced more than 200 Mbits of OTPs. According to Vladimir Kotelnikov in 1941 and Claude Shannon in 1945, one-time pad sequences are theoretically-perfect and unbreakable, provided specific rules are adhered to. Two other techniques for generating random binary sequences were researched; a new circuit element, memristance was incorporated in a Chua chaos oscillator, and a fractional-order Lorenz chaos system with order less than three. Quantum computing will present many problems to cryptographic system security when existing systems are upgraded in the near future. The only existing encoding system that will resist cryptanalysis by this system is the unconditionally-secure one-time pad encryption

Electronic Nanodevices

The start of high-volume production of field-effect transistors with a feature size below 100 nm at the end of the 20th century signaled the transition from microelectronics to nanoelectronics. Since then, downscaling in the semiconductor industry has continued until the recent development of sub-10 nm technologies. The new phenomena and issues as well as the technological challenges of the fabrication and manipulation at the nanoscale have spurred an intense theoretical and experimental research activity. New device structures, operating principles, materials, and measurement techniques have emerged, and new approaches to electronic transport and device modeling have become necessary. Examples are the introduction of vertical MOSFETs in addition to the planar ones to enable the multi-gate approach as well as the development of new tunneling, high-electron mobility, and single-electron devices. The search for new materials such as nanowires, nanotubes, and 2D materials for the transistor channel, dielectrics, and interconnects has been part of the process. New electronic devices, often consisting of nanoscale heterojunctions, have been developed for light emission, transmission, and detection in optoelectronic and photonic systems, as well for new chemical, biological, and environmental sensors. This Special Issue focuses on the design, fabrication, modeling, and demonstration of nanodevices for electronic, optoelectronic, and sensing applications

Exploring emergence in interconnected ferromagnetic nanoring arrays

Emergent interactions in periodic, artificial ferromagnetic nanostructures is well explored for magnetic systems such as artificial spin ices (ASI). This work presents a novel approach of an interconnected array of ferromagnetic nanorings to harness emergence in a dynamic system for functionality. Magnetic nanorings have two preferred configurations of magnetisation – ‘vortex’ that contains no domain walls (DWs) and ‘onion’ state with two DWs. In-plane applied rotating fields move DWs around a ring. The junction between interconnected rings presents a pinning potential that must be overcome to continue DW motion. In an ensemble, such as an array of interconnected rings, a sufficiently high field gives unimpeded DW motion. Under a sufficiently low field, no DWs de-pin. Both conserve DW population. Between these limits, de-pinning is probabilistic and field dependent. When one DW in an ‘onion’ state is pinned and the other de-pins, annihilation of DWs will occur and rings convert from ‘onion’ to ‘vortex’. Micromagnetic modelling also shows a DW de-pinning from a junction adjacent to a ‘vortex’ ring repopulates it with DWs. Analytical modelling of DW population revealed an equilibrium that varies non- monotonically with de-pinning probability and varies with array size and geometry. Polarised neutron reflectometry (PNR) and MOKE magnetometry measured arrays of permalloy nanorings. Magnetisation as a function of applied rotating field strength confirmed a non-monotonic response. Magnetic force microscopy (MFM) and photoemission electron microscopy (PEEM) allowed direct observation of DW configurations, revealing: highly ordered arrangements of ‘onion’ states at saturation; minor changes in DW population with low and high strength rotating fields; DW loss and breakdown in long-range order with intermediate fields. Imaging showed junctions produce behaviour analogous to emergent vertex configurations in ASIs. Interconnected nanoring arrays show good candidacy for novel computing architectures, such as reservoir computing, given their dynamic tuneability, non-linear response to an external stimulus, scalability, fading memory and repeatability

Simulation and programming strategies to mitigate device non-idealities in memristor based neuromorphic systems

Since its inception, resistive random access memory (RRAM) has widely been regarded as a promising technology, not only for its potential to revolutionize non-volatile data storage by bridging the speed gap between traditional solid state drives (SSD) and dynamic random access memory (DRAM), but also for the promise it brings to in-memory and neuromorphic computing. Despite the potential, the design process of RRAM neuromorphic arrays still finds itself in its infancy, as reliability (retention, endurance, programming linearity) and variability (read-to-read, cycle-to-cycle and device-to-device) issues remain major hurdles for the mainstream implementation of these systems. One of the fundamental stages of neuromorphic design is the simulation stage. In this thesis, a simulation framework for evaluating the impact of RRAM non-idealities on NNs, that emphasizes flexibility and experimentation in NN topology and RRAM programming conditions is coded in MATLAB, making full use of its various toolboxes. Using these tools as the groundwork, various RRAM non-idealities are comprehensively measured and their impact on both inference and training accuracy of a pattern recognition system based on the MNIST handwritten digits dataset are simulated. In the inference front, variability originated from different sources (read-to-read and programming-to-programming) are statistically evaluated and modelled for two different device types: filamentary and non-filamentary. Based on these results, the impact of various variability sources on inference are simulated and compared, showing much more pronounced variability in the filamentary device compared to its non-filamentary counterpart. The staged programming scheme is introduced as a method to improve linearity and reduce programming variability, leading to negligible accuracy loss in non-filamentary devices. Random telegraph noise (RTN) remains the major source of read variability in both devices. These results can be explained by the difference in switching mechanisms of both devices. In training, non-idealities such as conductance stepping and cycle-to-cycle variability are characterized and their impact on the training of NNs based on backpropagation are independently evaluated. Analysing the change of weight distributions during training reveals the different impacts on the SET and RESET processes. Based on these findings, a new selective programming strategy is introduced for the suppression of non-idealities impact on accuracy. Furthermore, the impact of these methods are analysed between different NN topologies, including traditional multi-layer perceptron (MLP) and convolutional neural network (CNN) configurations. Finally, the new dynamic weight range rescaling methodology is introduced as a way of not only alleviating the constraints imposed in hardware due to the limited conductance range of RRAM in training, but also as way of increasing the flexibility of RRAM based deep synaptic layers to different sets of data