A Compact CMOS Memristor Emulator Circuit and its Applications

Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic circuits. We introduce a compact CMOS circuit that emulates idealized memristor characteristics and can bridge the gap between concepts to chip-scale realization by transcending device challenges. The CMOS memristor circuit embodies a two-terminal variable resistor whose resistance is controlled by the voltage applied across its terminals. The memristor 'state' is held in a capacitor that controls the resistor value. This work presents the design and simulation of the memristor emulation circuit, and applies it to a memcomputing application of maze solving using analog parallelism. Furthermore, the memristor emulator circuit can be designed and fabricated using standard commercial CMOS technologies and opens doors to interesting applications in neuromorphic and machine learning circuits.Comment: Submitted to International Symposium of Circuits and Systems (ISCAS) 201

Memristive Grid for Maze Solving

Memcomputing represents a novel form of neuro-oriented signal processing that uses the memristor as a key element. In this chapter, a memristive grid is developed in order to achieve the specific task of solving mazes. This is done by resorting to the dynamic behavior of the memristance in order to find the shortest path that determines trajectory from entrance to exit. The structure of the maze is mapped onto the memristive grid, which is formed by memristors that are defined by fully analytical charge-controlled functions. The dependance on the electric charge permits to analyze the variation of the branch memristance of the grid as a function of time. As a result of the dynamic behavior of the developed memristor model, the shortest path is formed by those memristive branches exhibiting the fastest memristance change. Special attention is given to achieve a realistic implementation of the fuses of the grid, which are formed by an anti-series connection of memristors and CMOS circuitry. HSPICE is used in combination with MATLAB to establish the simulation flow of the memristive grid. Besides, the memristor model is recast in VERILOG-A, a high-level hardware description language for analog circuits

Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

Memcomputing: a computing paradigm to store and process information on the same physical platform

In present day technology, storing and processing of information occur on physically distinct regions of space. Not only does this result in space limitations; it also translates into unwanted delays in retrieving and processing of relevant information. There is, however, a class of two-terminal passive circuit elements with memory, memristive, memcapacitive and meminductive systems -- collectively called memelements -- that perform both information processing and storing of the initial, intermediate and final computational data on the same physical platform. Importantly, the states of these memelements adjust to input signals and provide analog capabilities unavailable in standard circuit elements, resulting in adaptive circuitry, and providing analog massively-parallel computation. All these features are tantalizingly similar to those encountered in the biological realm, thus offering new opportunities for biologically-inspired computation. Of particular importance is the fact that these memelements emerge naturally in nanoscale systems, and are therefore a consequence and a natural by-product of the continued miniaturization of electronic devices. We will discuss the various possibilities offered by memcomputing, discuss the criteria that need to be satisfied to realize this paradigm, and provide an example showing the solution of the shortest-path problem and demonstrate the healing property of the solution path.Comment: The first part of this paper has been published in Nature Physics 9, 200-202 (2013). The second part has been expanded and is now included in arXiv:1304.167

Exploring Spin-transfer-torque devices and memristors for logic and memory applications

As scaling CMOS devices is approaching its physical limits, researchers have begun exploring newer devices and architectures to replace CMOS. Due to their non-volatility and high density, Spin Transfer Torque (STT) devices are among the most prominent candidates for logic and memory applications. In this research, we first considered a new logic style called All Spin Logic (ASL). Despite its advantages, ASL consumes a large amount of static power; thus, several optimizations can be performed to address this issue. We developed a systematic methodology to perform the optimizations to ensure stable operation of ASL. Second, we investigated reliable design of STT-MRAM bit-cells and addressed the conflicting read and write requirements, which results in overdesign of the bit-cells. Further, a Device/Circuit/Architecture co-design framework was developed to optimize the STT-MRAM devices by exploring the design space through jointly considering yield enhancement techniques at different levels of abstraction. Recent advancements in the development of memristive devices have opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this research, we utilized swarm intelligence of memristive networks to perform image edge detection. First, we proposed a hardware-friendly algorithm for image edge detection based on ant colony. Next, we designed the image edge detection algorithm using memristive networks

Unconventional Computing and Music: An Investigation into Harnessing Physarum polycephalum

This thesis presents an investigation into developing musical systems with an Unconventional Computing substrate. Computer musicians have found it difficult to access the field of Unconventional Computing, which is likely due to its resource-intensive and complex nature. However, ongoing research is establishing the myxomycete Physarum polycephalum as a universally-accessible and versatile biological computing substrate. As such, the organism is a potential gateway for computer musicians to begin experimenting with aspects of Unconventional Computing. Physarum polycephalum, in its vegetative plasmodium form, is an amorphous unicellular organism that can respond with natural parallelism to the environmental conditions that surround it. This thesis explores the challenges and opportunities related to developing musical systems with Physarum polycephalum. As this area of inquiry is in its infancy, the research took inspiration from a common approach in Unconventional Computing: a journey of exploration and discovery. This journey consisted of a selection of waypoints that provided direction while allowing the research to explore applications of Physarum polycephalum in order to establish how it may be useful in Computer Music. These waypoints guided the research from adapting established prototypes for musical application to developing purpose-made musical demonstrators for use outside of the laboratory. Thus, the thesis reports on a series of Computer Music systems that explore one or more features of Physarum polycephalum's behaviour and physiology. First, the text presents an approach to algorithmic composition that exploits the organism's ability to form and reconfigure graph-like structures. Next, the thesis reports on systems that harness the plasmodium's electrical potential oscillations for sound synthesis and compositional tools. Finally, the thesis presents musical devices that encompass living plasmodium as electrical components. Where applicable, the thesis includes artefacts from demonstrations of these systems, some of which were developed in collaboration with a composer. The findings from this journey demonstrate that Physarum polycephalum is an appropriate substrate for computer musicians wanting to explore Unconventional Computing approaches creatively. Although Physarum polycephalum is relatively robust as a biological substrate, several obstacles arose during this project. This research addressed such obstacles by reviewing and selecting approaches that maintained the organism's accessibility to computer musicians. As a result, the work suggests methods for developing systems with the organism that are practical for the average music technologist and also beneficial to the wider group of scientists investigating Physarum polycephalum for other purposes.Plymouth University HumPA Studentshi

Investigation of Transport Behavior in Two-Dimensional Ferroelectric Heterostructures

This dissertation summarizes an investigation of the polarization-related electronic transport behavior in the ferroelectric thin films and two-dimensional (2D) materials heterostructures using Scanning Probe Microscopy (SPM) techniques. The polarization-related resistive switching in hafnium oxide thin films-based ferroelectric tunnel junction has been demonstrated by employing semiconducting MoS2 as a top electrode. We explored a coupling between the semiconducting properties of MoS2 and the polarization of Hf0.5Zr0.5O2 resulted in an enhanced tunneling electroresistance effect of up to 3 orders of magnitude. These results provide a possible pathway for the fabrication of high-density non-volatile memory devices. These results are presented in Chapter 3. Resistive switching control using conducting domain walls as functional elements has been investigated using graphene/LiNbO3 heterostructures. One approach involves the modulation of resistance through the manipulation of domain wall density using super-coercive voltage. This approach requires higher energy to switch the polarization and can induce high leakage current that makes it deleterious. To overcome this drawback, we have developed a new approach that involves tuning of domain wall conductivity by a sub-coercive voltage without altering the domain configuration. These results are presented in Chapter 4. Chapter 5 describes modulation of the transport behavior of 2D MoS2 junctions by mechanical stress induced by the sharp probe of atomic force microscope (AFM). We show that the junction resistance can be reversibly tuned by up to 4 orders of magnitude by altering the mechanical force applied via AFM tip. Additionally, we show that AFM tip generates strain gradient inducing flexoelectric effect that leads to an enhancement of photovoltaic effect. Finally, we have discovered stable room temperature ferroelectricity with out-of-plane polarization in trigonally distorted 1T”-MoS2. Here, the polarization switching has been realized by the mechanical load applied via AFM probe. The piezoelectric and the electrical properties of MoS2 flakes are probed. Moreover, we show that flipped flakes of 1T”-MoS2 samples consist of monolayers of randomly oriented polarization, showing the possibility of head-head or tail-tail configuration. These results are presented in Chapter 6. Advisor: Alexei Gruverma

An investigation into alternative methods for the simulation and analysis of growth models

Complex systems are a rapidly increasing area of research covering numerous disciplines including economics and even cancer research, as such the optimisation of the simulations of these systems is important. This thesis will look specifically at two cellular automata based growth models the Eden growth model and the Invasion Percolation model. These models tend to be simulated storing the cluster within a simple array. This work demonstrates that for models which are highly sparse this method has drawbacks in both the memory consumed and the overall runtime of the system. It demonstrates that more modern data structures such as the HSH tree can offer considerable benefits to these models.Next, instead of optimising the software simulation of the Eden growth model, we detail a memristive-based cellular automata architecture that is capable of simulating the Eden growth model called the MEden model. It is demonstrated that not only is this method faster, up to 12; 704 times faster than the software simulation, it also allows for the same system to be used for the simulation of both EdenB and EdenC clusters without the need to be reconfigured; this is achieved through the use of two different parameters present in the model Pmax and Pchance. Giving the model a broader range of possible clusters which can aid with Monte-Carlo simulations of the model.Finally, two methods were developed to be able to identify a difference between fractally identical clusters; connected component labelling and convolution neural networks are the methods used to achieve this. It is demonstrated that both of these methods allow for the identification of individual Eden clusters able to classify them as either an EdenA, EdenB, or EdenC cluster, a highly nontrivial matter with current methods. It is also able to tell when a cluster was not an Eden cluster even though it fell in the fractal range of an Eden cluster. These features mean that the verification of a new method for the simulation of the Eden model could now be automated