The Efficacy of Programming Energy Controlled Switching in Resistive Random Access Memory (RRAM)

Current state-of-the-art memory technologies such as FLASH, Static Random Access Memory (SRAM) and Dynamic RAM (DRAM) are based on charge storage. The semiconductor industry has relied on cell miniaturization to increase the performance and density of memory technology, while simultaneously decreasing the cost per bit. However, this approach is not sustainable because the charge-storage mechanism is reaching a fundamental scaling limit. Although stack engineering and 3D integration solutions can delay this limit, alternate strategies based on non-charge storage mechanisms for memory have been introduced and are being actively pursued. Resistive Random Access Memory (RRAM) has emerged as one of the leading candidates for future high density non-volatile memory. The superior scalability of RRAMs is based on the highly localized active switching region and filamentary conductive path. Coupled with its simple structure and compatibility with complementary metal oxide semiconductor (CMOS) processes; RRAM cells have demonstrated switching performance comparable to volatile memory technologies such as DRAMs and SRAMs. However, there are two serious barriers to RRAM commercialization. The first is the variability of the resistance state which is associated with the inherent randomness of the resistive switching mechanism. The second is the filamentary nature of the conductive path which makes it susceptible to noise. In this experimental thesis, a novel program-verify (P-V) technique was developed with the objective to specifically address the programming errors and to provide solutions to the most challenging issues associated with these intrinsic failures in current RRAM technology. The technique, called Compliance-free Ultra-short Smart Pulse Programming (CUSPP), utilizes sub-nanosecond pulses in a compliance-free setup to minimize the programming energy delivered per pulse. In order to demonstrate CUSPP, a custom-built picosecond pulse generator and feedback control circuit was designed. We achieved high (108 cycles) endurance with state verification for each cycle and established high-speed performance, such as 100 ps write/erase speed and 500 kHz cycling rate of HfO2-based RRAM cells. We also investigate switching failure and the short-term instability of the RRAM using CUSPP

Design of a test platform for studying radiation effects on SPI FRAM and I² C CMOS non-volatile memories

Electronic components are often exposed to external radiation that may interfere with their normal operation. This becomes particularly relevant for high-scale of integration parts and when said components are used in harsh environments such as in satellites and space ships, aviation, military applications or in heavy and automotive industries. Several design and construction techniques have been developed to overcome, or at least minimize, the impact of external factors on electronic components, so ESD (Electro-Static Discharge), EMI (Electro-Magnetic Interference) or RFI (Radio-Frequency Interference) protection is usually built-in into mission-critical electronic components like microcontrollers, FPGAs or memory chips. The Computer Architecture Department at FDI is carrying out the READAR-II project with the aim of developing techniques to improve the resistance of different types of components used in space missions. In particular, and as a first step, the study on semiconductor-based memories. This BSc. Thesis goal lies within the READAR-II research project objective and focuses on building a test platform to evaluate the radiation effects on non-volatile memories

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

Applications of memristors in conventional analogue electronics

This dissertation presents the steps employed to activate and utilise analogue memristive devices in conventional analogue circuits and beyond. TiO2 memristors are mainly utilised in this study, and their large variability in operation in between similar devices is identified. A specialised memristor characterisation instrument is designed and built to mitigate this issue and to allow access to large numbers of devices at a time. Its performance is quantified against linear resistors, crossbars of linear resistors, stand-alone memristive elements and crossbars of memristors. This platform allows for a wide range of different pulsing algorithms to be applied on individual devices, or on crossbars of memristive elements, and is used throughout this dissertation. Different ways of achieving analogue resistive switching from any device state are presented. Results of these are used to devise a state-of-art biasing parameter finder which automatically extracts pulsing parameters that induce repeatable analogue resistive switching. IV measurements taken during analogue resistive switching are then utilised to model the internal atomic structure of two devices, via fittings by the Simmons tunnelling barrier model. These reveal that voltage pulses modulate a nano-tunnelling gap along a conical shape. Further retention measurements are performed which reveal that under certain conditions, TiO2 memristors become volatile at short time scales. This volatile behaviour is then implemented into a novel SPICE volatile memristor model. These characterisation methods of solid-state devices allowed for inclusion of TiO2 memristors in practical electronic circuits. Firstly, in the context of large analogue resistive crossbars, a crosspoint reading method is analysed and improved via a 3-step technique. Its scaling performance is then quantified via SPICE simulations. Next, the observed volatile dynamics of memristors are exploited in two separate sequence detectors, with applications in neuromorphic engineering. Finally, the memristor as a programmable resistive weight is exploited to synthesise a memristive programmable gain amplifier and a practical memristive automatic gain control circuit.Open Acces

Feasibility of self-structured current accessed bubble devices in spacecraft recording systems

The self-structured, current aperture approach to magnetic bubble memory is described. Key results include: (1) demonstration that self-structured bubbles (a lattice of strongly interacting bubbles) will slip by one another in a storage loop at spacings of 2.5 bubble diameters, (2) the ability of self-structured bubbles to move past international fabrication defects (missing apertures) in the propagation conductors (defeat tolerance), and (3) moving bubbles at mobility limited speeds. Milled barriers in the epitaxial garnet are discussed for containment of the bubble lattice. Experimental work on input/output tracks, storage loops, gates, generators, and magneto-resistive detectors for a prototype device are discussed. Potential final device architectures are described with modeling of power consumption, data rates, and access times. Appendices compare the self-structured bubble memory from the device and system perspectives with other non-volatile memory technologies

Enabling a reliable STT-MRAM main memory simulation

STT-MRAM is a promising new memory technology with very desirable set of properties such as non-volatility, byte-addressability and high endurance. It has the potential to become the universal memory that could be incorporated to all levels of memory hierarchy. Although STT-MRAM technology got significant attention of various major memory manufacturers, to this day, academic research of STT-MRAM main memory remains marginal. This is mainly due to the unavailability of publicly available detailed timing parameters which are required to perform a cycle accurate main memory simulation. Our study presents a detailed analysis of STT-MRAM main memory timing and propose an approach to perform a reliable system level simulation of the memory technology. We seamlessly incorporate STT-MRAM timing parameters into DRAMSim2 memory simulator and use it as a part of the simulation infrastructure of the high-performance computing (HPC) systems. Our results suggests that, STT-MRAM main memory would provide performance comparable to DRAM, while opening up various opportunities for HPC system improvements. Most importantly, our study enables researchers to conduct reliable system level research on STT-MRAM main memory, and to explore the opportunities that this technology has to offer.This work was supported by BSC, Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). This work has also received funding from the European Union's Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578). The authors wish to thank Terry Hulett, Duncan Bennett and Ben Cooke from Everspin Technologies Inc., for their technical support.Peer ReviewedPostprint (author's final draft

Power Profile Obfuscation using RRAMs to Counter DPA Attacks

Side channel attacks, such as Differential Power Analysis (DPA), denote a special class of attacks in which sensitive key information is unveiled through information extracted from the physical device executing a cryptographic algorithm. This information leakage, known as side channel information, occurs from computations in a non-ideal system composed of electronic devices such as transistors. Power dissipation is one classic side channel source, which relays information of the data being processed. DPA uses statistical analysis to identify data-dependent correlations in sets of power measurements. Countermeasures against DPA focus on hiding or masking techniques at different levels of design abstraction and are typically associated with high power and area cost. Emerging technologies such as Resistive Random Access Memory (RRAM), offer unique opportunities to mitigate DPAs with their inherent memristor device characteristics such as variability in write time, ultra low power (0.1-3 pJ/bit), and high density (4F2). In this research, an RRAM based architecture is proposed to mitigate the DPA attacks by obfuscating the power profile. Specifically, a dual RRAM based memory module masks the power dissipation of the actual transaction by accessing both the data and its complement from the memory in tandem. DPA attack resiliency for a 128-bit AES cryptoprocessor using RRAM and CMOS memory modules is compared against baseline CMOS only technology. In the proposed AES architecture, four single port RRAM memory units store the intermediate state of the encryption. The correlation between the state data and sets of power measurement is masked due to power dissipated from inverse data access on dual RRAM memory. A customized simulation framework is developed to design the attack scenarios using Synopsys and Cadence tool suites, along with a Hamming weight DPA attack module. The attack mounted on a baseline CMOS architecture is successful and the full key is recovered. However, DPA attacks mounted on the dual CMOS and RRAM based AES cryptoprocessor yielded unsuccessful results with no keys recovered, demonstrating the resiliency of the proposed architecture against DPA attacks