DESIGN AND TEST OF DIGITAL CIRCUITS AND SYSTEMS USING CMOS AND EMERGING RESISTIVE DEVICES

The memristor is an emerging nano-device. Low power operation, high density, scalability, non-volatility, and compatibility with CMOS Technology have made it a promising technology for memory, Boolean implementation, computing, and logic systems. This dissertation focuses on testing and design of such applications. In particular, we investigate on testing of memristor-based memories, design of memristive implementation of Boolean functions, and reliability and design of neuromorphic computing such as neural network. In addition, we show how to modify threshold logic gates to implement more functions. Although memristor is a promising emerging technology but is prone to defects due to uncertainties in nanoscale fabrication. Fast March tests are proposed in Chapter 2 that benefit from fast write operations. The test application time is reduced significantly while simultaneously reducing the average test energy per cell. Experimental evaluation in 45 nm technology show a speed-up of approximately 70% with a decrease in energy by approximately 40%. DfT schemes are proposed to implement the new test methods. In Chapter 3, an Integer Linear Programming based framework to identify current-mode threshold logic functions is presented. It is shown that threshold logic functions can be implemented in CMOS-based current mode logic with reduced transistor count when the input weights are not restricted to be integers. Experimental results show that many more functions can be implemented with predetermined hardware overhead, and the hardware requirement of a large percentage of existing threshold functions is reduced when comparing to the traditional CMOS-based threshold logic implementation. In Chapter 4, a new method to implement threshold logic functions using memristors is presented. This method benefits from the high range of memristor’s resistivity which is used to define different weight values, and reduces significantly the transistor count. The proposed approach implements many more functions as threshold logic gates when comparing to existing implementations. Experimental results in 45 nm technology show that the proposed memristive approach implements threshold logic gates with less area and power consumption. Finally, Chapter 5 focuses on current-based designs for neural networks. CMOS aging impacts the total synaptic current and this impacts the accuracy. Chapter 5 introduces an enhanced memristive crossbar array (MCA) based analog neural network architecture to improve reliability due to the aging effect. A built-in current-based calibration circuit is introduced to restore the total synaptic current. The calibration circuit is a current sensor that receives the ideal reference current for non-aged column and restores the reduced sensed current at each column to the ideal value. Experimental results show that the proposed approach restores the currents with less than 1% precision, and the area overhead is negligible

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

Null Convention Logic applications of asynchronous design in nanotechnology and cryptographic security

This dissertation presents two Null Convention Logic (NCL) applications of asynchronous logic circuit design in nanotechnology and cryptographic security. The first application is the Asynchronous Nanowire Reconfigurable Crossbar Architecture (ANRCA); the second one is an asynchronous S-Box design for cryptographic system against Side-Channel Attacks (SCA). The following are the contributions of the first application: 1) Proposed a diode- and resistor-based ANRCA (DR-ANRCA). Three configurable logic block (CLB) structures were designed to efficiently reconfigure a given DR-PGMB as one of the 27 arbitrary NCL threshold gates. A hierarchical architecture was also proposed to implement the higher level logic that requires a large number of DR-PGMBs, such as multiple-bit NCL registers. 2) Proposed a memristor look-up-table based ANRCA (MLUT-ANRCA). An equivalent circuit simulation model has been presented in VHDL and simulated in Quartus II. Meanwhile, the comparison between these two ANRCAs have been analyzed numerically. 3) Presented the defect-tolerance and repair strategies for both DR-ANRCA and MLUT-ANRCA. The following are the contributions of the second application: 1) Designed an NCL based S-Box for Advanced Encryption Standard (AES). Functional verification has been done using Modelsim and Field-Programmable Gate Array (FPGA). 2) Implemented two different power analysis attacks on both NCL S-Box and conventional synchronous S-Box. 3) Developed a novel approach based on stochastic logics to enhance the resistance against DPA and CPA attacks. The functionality of the proposed design has been verified using an 8-bit AES S-box design. The effects of decision weight, bitstream length, and input repetition times on error rates have been also studied. Experimental results shows that the proposed approach enhances the resistance to against the CPA attack by successfully protecting the hidden key --Abstract, page iii

BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

Towards Data Reliable, Low-Power, and Repairable Resistive Random Access Memories

A series of breakthroughs in memristive devices have demonstrated the potential of memristor arrays to serve as next generation resistive random access memories (ReRAM), which are fast, low-power, ultra-dense, and non-volatile. However, memristors' unique device characteristics also make them prone to several sources of error. Owing to the stochastic filamentary nature of memristive devices, various recoverable errors can affect the data reliability of a ReRAM. Permanent device failures further limit the lifetime of a ReRAM. This dissertation developed low-power solutions for more reliable and longer-enduring ReRAM systems. In this thesis, we first look into a data reliability issue known as write disturbance. Writing into a memristor in a crossbar could disturb the stored values in other memristors that are on the same memory line as the target cell. Such disturbance is accumulative over time which may lead to complete data corruption. To address this problem, we propose the use of two regular memristors on each word to keep track of the disturbance accumulation and trigger a refresh to restore the weakened data, once it becomes necessary. We also investigate the considerable variation in the write-time characteristics of individual memristors. With such variation, conventional fixed-pulse write schemes not only waste significant energy, but also cannot guarantee reliable completion of the write operations. We address such variation by proposing an adaptive write scheme that adjusts the width of the write pulses for each memristor. Our scheme embeds an online monitor to detect the completion of a write operation and takes into account the parasitic effect of line-shared devices in access-transistor-free memristive arrays. We further investigate the use of this method to shorten the test time of memory march algorithms by eliminating the need of a verifying read right after a write, which is commonly employed in the test sequences of march algorithms.Finally, we propose a novel mechanism to extend the lifetime of a ReRAM by protecting it against hard errors through the exploitation of a unique feature of bipolar memristive devices. Our solution proposes an unorthodox use of complementary resistive switches (a particular implementation of memristive devices) to provide an ``in-place spare'' for each memory cell at negligible extra cost. The in-place spares are then utilized by a repair scheme to repair memristive devices that have failed at a stuck-at-ON state at a page-level granularity. Furthermore, we explore the use of in-place spares in lieu of other memory reliability and yield enhancement solutions, such as error correction codes (ECC) and spare rows. We demonstrate that with the in-place spares, we can yield the same lifetime as a baseline ReRAM with either significantly fewer spare rows or a lighter-weight ECC, both of which can save on energy consumption and area

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

Cerium oxide based resistive random access memory devices

Resistive Random Access Memory (RRAM) is an emerging technology of non-volatile memory (NVM). Although the observation of metal oxide that can undergo an abrupt insulator-metal transition into a conductive state has been known for over 40 years, researchers started investigating those materials for memory applications in late 1990s. It has been considered as the next generation memory technology to replace current flash memory because RRAM has demonstrated feasible switching characteristics and potential to build high density arrays and also RRAM is also compatible with contemporary CMOS processes, which means RRAM can be integrated into current CMOS chips. While the structure of RRAM is a simple metal-insulator-metal (MIM) device, there are numerous materials that exhibit resistive switching. The switching behavior is not only dependent on the switching layer materials but also dependent on the choice of metal electrodes and their interfacial properties. Many metal oxides such as hafnium oxide, titanium oxide, aluminum oxide, nickel oxide (NiO), tantalum oxide and etc. have been studied in details; however, some materials are unexplored such as cerium oxide. In addition to nonvolatile storage applications, RRAM is considered as one of essential elements for advancing neuromorphic computing because of its analog switching and retention characteristics. This thesis investigated CeO[subscript x]-based RRAMs, from its fundamental device characteristics to neuromorphic applications.Electrical and Computer Engineerin