In-Memory Computing by Using Nano-ionic Memristive Devices

By reaching to the CMOS scaling limitation based on the Moore’s law and due to the increasing disparity between the processing units and memory performance, the quest is continued to find a suitable alternative to replace the conventional technology. The recently discovered two terminal element, memristor, is believed to be one of the most promising candidates for future very large scale integrated systems. This thesis is comprised of two main parts, (Part I) modeling the memristor devices, and (Part II) memristive computing. The first part is presented in one chapter and the second part of the thesis contains five chapters. The basics and fundamentals regarding the memristor functionality and memristive computing are presented in the introduction chapter. A brief detail of these two main parts is as follows: Part I: Modeling- This part presents an accurate model based on the charge transport mechanisms for nanoionic memristor devices. The main current mechanism in metal/insulator/metal (MIM) structures are assessed, a physic-based model is proposed and a SPICE model is presented and tested for four different fabricated devices. An accuracy comparison is done for various models for Ag/TiO2/ITO fabricated device. Also, the functionality of the model is tested for various input signals. Part II: Memristive computing- Memristive computing is about utilizing memristor to perform computational tasks. This part of the thesis is divided into neuromorphic, analog and digital computing schemes with memristor devices. – Neuromorphic computing- Two chapters of this thesis are about biologicalinspired memristive neural networks using STDP-based learning mechanism. The memristive implementation of two well-known spiking neuron models, Hudgkin-Huxley and Morris-Lecar, are assessed and utilized in the proposed memristive network. The synaptic connections are also memristor devices in this design. Unsupervised pattern classification tasks are done to ensure the right functionality of the system. – Analog computing- Memristor has analog memory property as it can be programmed to different memristance values. A novel memristive analog adder is designed by Continuous Valued Number System (CVNS) scheme and its circuit is comprised of addition and modulo blocks. The proposed analog adder design is explained and its functionality is tested for various numbers. It is shown that the CVNS scheme is compatible with memristive design and the environment resolution can be adjusted by the memristance ratio of the memristor devices. – Digital computing- Two chapters are dedicated for digital computing. In the first one, a development over IMPLY-based logic with memristor is provided to implement a 4:2 compressor circuit. In the second chapter, A novel resistive over a novel mirrored memristive crossbar platform. Different logic gates are designed with the proposed memristive logic method and the simulations are provided with Cadence to prove the functionality of the logic. The logic implementation over a mirrored memristive crossbars is also assessed

Bipolar resistive switching of bi-layered Pt/Ta2O5/TaOx/Pt RRAM : physics-based modelling, circuit design and testing

Over the last few years, the non-volatile memories (NVM) have been dominating the research of the storage elements. The resistance random-access memory (RRAM) and the memristor that employs the resistive switching (RS) mechanism appear to be potential candidates for NVM. Among the RS materials that were reported is the TaOx which showed surprising RS performance. This oxide material has been widely used to construct a metal-insulator-semiconductor-metal (MISM) RRAM which can be referred to as bi-layered RRAM. This bi-layered RRAM consists of TaOx as a bulk material and Ta2O5 as an insulator layer, sandwiched between two platinum electrodes to form Pt/Ta2O5/TaOx/Pt RRAM. However, a physics-based mathematical model of this RRAM is required to further study the detailed physics behind its conduction mechanism and the RS process. In addition to the mathematical model, a SPICE model is also required to understand the behaviour of this bi-layered RRAM device when integrated in memory design for the future generation storage devices or when used in RRAM-based circuit applications. This doctoral research presents novel mathematical and SPICE models of a bipolar resistive switching (BRS) of the Pt/Ta2O5/TaOx/Pt bi-layered RRAM. For this purpose, MATLAB and LTSPICE are used to design the mathematical and the SPICE bi-layered RRAM models, respectively, and the obtained simulation results for both models are compared with the experimental data from SAMSUNG labs. The novelty of the mathematical model lies in incorporating the tunnelling probability factor (TPF) between the semiconductor and the metal layers and therefore, demonstrating its effect on the conduction mechanism. In addition, the effect of continuous variation of the interface traps densities and the ideality factor during BRS is modelled using the semiconductor properties and the characteristics of the metal-insulator-semiconductor (MIS) system. Thus, the model emphasizes the dependency of the device current on the physical characteristics of the insulator layer. Moreover, the electric field equation for the active region is derived for the MISM structure which is used together with Mott and Gurney rigid point-ion model and Joule heating effect to model the oxygen ion migration mechanism. Finally, the model also demonstrates the self-limiting growth of the doped region. The proposed SPICE model emphasizes the impact of the change in the switching layer thickness on the device behaviour at low resistance state (LRS), high resistance state (HRS), and the transitional period. The validity of the SPICE model is verified through using three different sets of experimental data from Pt/Ta2O5/TaOx/Pt RRAM with switching layer thickness smaller than 5 nm. The SPICE model reproduced all the major features from the experimental results for the SET and RESET processes and also the asymmetric and the symmetric characteristics in HRS and LRS, respectively. The SPICE model matches the measured experimental results with an average error of < 11%. It also showed stable behaviour for its HRS and LRS regions under different types of input signals. The model is parameterized in order to fit into Ta2O5/TaOx RRAM devices with switching layer thickness smaller than 5 nm, thus, facilitating the model usage. The SPICE model can be included in the SPICE-compatible circuit simulation and is suitable for the exploration of the Ta2O5/TaOx bi-layered RRAM device performance at circuit level. At the end of the research, a metal-insulator-metal (MIM) RRAM SPICE model of Ta/TaOx/Pt is developed which can be used in the future work to compare between the MISM and MIM TaOx-based RRAM devices

Bipolar resistive switching of bi-layered Pt/Ta2O5/TaOx/Pt RRAM : physics-based modelling, circuit design and testing

Over the last few years, the non-volatile memories (NVM) have been dominating the research of the storage elements. The resistance random-access memory (RRAM) and the memristor that employs the resistive switching (RS) mechanism appear to be potential candidates for NVM. Among the RS materials that were reported is the TaOx which showed surprising RS performance. This oxide material has been widely used to construct a metal-insulator-semiconductor-metal (MISM) RRAM which can be referred to as bi-layered RRAM. This bi-layered RRAM consists of TaOx as a bulk material and Ta2O5 as an insulator layer, sandwiched between two platinum electrodes to form Pt/Ta2O5/TaOx/Pt RRAM. However, a physics-based mathematical model of this RRAM is required to further study the detailed physics behind its conduction mechanism and the RS process. In addition to the mathematical model, a SPICE model is also required to understand the behaviour of this bi-layered RRAM device when integrated in memory design for the future generation storage devices or when used in RRAM-based circuit applications. This doctoral research presents novel mathematical and SPICE models of a bipolar resistive switching (BRS) of the Pt/Ta2O5/TaOx/Pt bi-layered RRAM. For this purpose, MATLAB and LTSPICE are used to design the mathematical and the SPICE bi-layered RRAM models, respectively, and the obtained simulation results for both models are compared with the experimental data from SAMSUNG labs. The novelty of the mathematical model lies in incorporating the tunnelling probability factor (TPF) between the semiconductor and the metal layers and therefore, demonstrating its effect on the conduction mechanism. In addition, the effect of continuous variation of the interface traps densities and the ideality factor during BRS is modelled using the semiconductor properties and the characteristics of the metal-insulator-semiconductor (MIS) system. Thus, the model emphasizes the dependency of the device current on the physical characteristics of the insulator layer. Moreover, the electric field equation for the active region is derived for the MISM structure which is used together with Mott and Gurney rigid point-ion model and Joule heating effect to model the oxygen ion migration mechanism. Finally, the model also demonstrates the self-limiting growth of the doped region. The proposed SPICE model emphasizes the impact of the change in the switching layer thickness on the device behaviour at low resistance state (LRS), high resistance state (HRS), and the transitional period. The validity of the SPICE model is verified through using three different sets of experimental data from Pt/Ta2O5/TaOx/Pt RRAM with switching layer thickness smaller than 5 nm. The SPICE model reproduced all the major features from the experimental results for the SET and RESET processes and also the asymmetric and the symmetric characteristics in HRS and LRS, respectively. The SPICE model matches the measured experimental results with an average error of < 11%. It also showed stable behaviour for its HRS and LRS regions under different types of input signals. The model is parameterized in order to fit into Ta2O5/TaOx RRAM devices with switching layer thickness smaller than 5 nm, thus, facilitating the model usage. The SPICE model can be included in the SPICE-compatible circuit simulation and is suitable for the exploration of the Ta2O5/TaOx bi-layered RRAM device performance at circuit level. At the end of the research, a metal-insulator-metal (MIM) RRAM SPICE model of Ta/TaOx/Pt is developed which can be used in the future work to compare between the MISM and MIM TaOx-based RRAM devices

Chalcogenide and metal-oxide memristive devices for advanced neuromorphic computing

Energy-intensive artificial intelligence (AI) is prevailing and changing the world, which requires energy-efficient computing technology. However, traditional AI driven by von Neumann computing systems suffers from the penalties of high-energy consumption and time delay due to frequent data shuttling. To tackle the issue, brain-inspired neuromorphic computing that performs data processing in memory is developed, reducing energy consumption and processing time. Particularly, some advanced neuromorphic systems perceive environmental variations and internalize sensory signals for localized in-senor computing. This methodology can further improve data processing efficiency and develop multifunctional AI products. Memristive devices are one of the promising candidates for neuromorphic systems due to their non-volatility, small size, fast speed, low-energy consumption, etc. In this thesis, memristive devices based on chalcogenide and metal-oxide materials are fabricated for neuromorphic computing systems. Firstly, a versatile memristive device (Ag/CuInSe2/Mo) is demonstrated based on filamentary switching. Non-volatile and volatile features are coexistent, which play multiple roles of non-volatile memory, selectors, artificial neurons, and artificial synapses. The conductive filaments’ lifetime was controlled to present both volatile and non-volatile behaviours. Secondly, the sensing functions (temperature and humidity) are explored based on Ag conductive filaments. An intelligent matter (Ag/Cu(In, Ga)Se2/Mo) endowing reconfigurable temperature and humidity sensations is developed for sensory neuromorphic systems. The device reversibly switches between two states with differentiable semiconductive and metallic features, demonstrating different responses to temperature and humidity variations. Integrated devices can be employed for intelligent electronic skin and in-sensor computing. Thirdly, the memristive-based sensing function of light was investigated. An optoelectronic synapse (ITO/ZnO/MoO3/Mo) enabling multi-spectrum sensitivity for machine vision systems is developed. For the first time, this optoelectronic synapse is practical for front-end retinomorphic image sensing, convolution processing, and back-end neuromorphic computing. This thesis will benefit the development of advanced neuromorphic systems pushing forward AI technology