Synthetic Heterosynaptic Plasticity Enhances the Versatility of Memristive Systems Emulating Bio-synapse Structure and Function

Memristive systems occur in nature and are hallmarked via pinched hysteresis, the difference in the forward and reverse pathways for a given phenomenon. For example, neurons of the human brain are composed of synapses which apply the properties of memristance for neuronal communication, learning, and memory consolidation. Modern technology has much to gain from the characteristics of memristive systems, including lower power operation, on-chip memory, and bio-inspired computing. What is more, a relationship between memristive systems and synaptic plasticity exists and can be investigated focusing on homosynaptic and heterosynaptic plasticity. Where homosynaptic plasticity applies to interactions between neurons at a synapse, heterosynaptic plasticity applies to an interneuron, a neuron that is not a part of the synapse, that modulates the neuronal interactions of synapses located elsewhere. Here, a synthetic synapse was used to study the heterosynaptic modulatory effects of osmotic stress via macromolecular crowding in the aqueous environment, membrane defects introduced from pH-sensitive secondary membrane species, and oxidative stress via oxidation of lipid species present in the membrane. Osmotic stress lowers the voltage threshold for alamethicin ion channels via depletion interactions and transmembrane water gradients. Secondary membrane species lowered the voltage threshold for alamethicin and lower pH environments enhanced the self-interaction between alamethicin monomers in a pore upon dissolution from the membrane. Oxidative stress created lipid species that compete for space in the polar-apolar interface of the lipid bilayer, leading to pore formation extending cell-free gene expression reactions. These findings help reveal how to environmentally modulate the synthetic synapse. Harnessing the power of memristive systems to create a biological computer enables the creation of new computers capable of adaptation, self-repair, and low-power operation while maintaining powerful computing and memory storage schemes

Hardware for Memristive Neuromorphic Systems with Reliable Programming and Online Learning

Alternative computing technologies are highly sought after due to limitations on transistor fabrication improvements. Fabricated memristive technology allows for a non-volatile analog memory for neuromorphic computing. In an integrated CMOS process, the synapse circuits designed for a spiking neuromorphic system can use memristors to regulate accumulation in the neuron circuits. Testing the fabricated memristive devices composed of hafnium oxide and developing a model to represent the key device characteristics lead to specific design choices in implementing the analog memory core of the synapse circuit. The circuits I designed for neuromorphic computing in this process take advantage of the unique capabilities of the memristive device to store a programmable analog memory reliably and efficiently. I designed the peripheral circuitry required including the circuits for programming the memristor and for online learning capabilities

Molecular dynamics simulations of nanoclusters in neuromorphic systems

Neuromorphic computing is a new computing paradigm that deals with computing tasks using inter-connected artificial neurons inspired by the natural neurons in the human brain. This computational architecture is more efficient in performing many complex tasks such a pattern recognition and has promise at overcoming some of the limitations of conventional computers. Among the emerging types of artificial neurons, a cluster-based neuromorphic device is a promising system with good costefficiency because of a simple fabrication process. This device functions using the formation and breakage of the connections (“synapses”) between clusters, driven by the bias voltage applied to the clusters. The mechanisms of the formation and breakage of these connections are thus of the utmost interest. In this thesis, the molecular dynamics simulation method is used to explore the mechanisms of the formation and breakage of the connections (“filaments”) between the clusters in a model of neuromorphic device. First, the Joule heating mechanism of filament breakage is explored using a model consisting of Au nanowire that connects two Au1415 clusters. Upon heating, the atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of the wire to graduallythin and then suddenly break. Most of the system remains crystalline during this process, but the centre becomes molten. The terminal clusters increase the melting point of the nanowires by fixing them and act as recrystallisation regions. A strong dependence of the breaking temperature is found not only on the width of the nanowires but also their length and atomic structure. Secondly, the bridge formation and thermal breaking processes between Au1415 clusters on a graphite substrate are also simulated. The bridging process , which can heal a broken filament, is driven by diffusion of gold along the graphite substrate. The characteristic times of bridge formation are explored at elevated simulation temperatures to estimate the longer characteristic times of formation at room-temperature conditions. The width of the bridge formed has a power-law dependence on the simulation time, and the mechanism is a combination of diffusion and viscous flow. Simulations of bridgebreaking are also conducted and reveal the existence of a voltage threshold that must be reached to activate the breakage. The role of the substrate in the bridge formation and breakage processes is revealed as a medium of diffusion. Lastly, to explore future potential cluster materials, the thermal behaviour of Pb-Al core-shell clusters is studied. The core and shell are found to melt separately. In fact, the core atoms of nanoclusters tend to escape their shells and partially cover them, leading to a preference for a segregated state. The melting point of the core can either be depressed or elevated, depending on the thickness of the shell due to different mechanisms

Silver Filament Formation/Dissolution Dynamics Through a Polymer/Ionic liquid Composite by Direct-write

A direct-write, electrochemical approach to the formation and dissolution of silver nanofilaments is demonstrated through a novel polymer electrolyte consisting of a UV-crosslinkable polymer, polyethylene glycol diacrylate (PEGDA) and an ionic liquid (IL), 1-butyl-3-methylimadozolium hexafluorophosphate ([BMIM]PF6). Nanofilaments are formed and dissolved at pre-programmed locations with a conductive atomic force microscope (c-AFM) using a custom script. Although the formation time generally decreases with increasing bias from 0.7 to 3.0 V, an unexpected non-monotonic maximum is observed ~2.0 V. At voltages approaching this region of inverted kinetics, IL electric double layers (EDLs) become detectable; thus, the increased nanofilament formation time can be attributed to electric field screening, which hinders silver electromigration and deposition. Scanning electron microscopy confirms that nanofilaments formed in this inverted region have significantly more lateral and diffuse features. Time dependent formation currents reveal two types of nanofilament growth dynamics: abrupt, where the resistance decreases sharply over as little as a few ms, and gradual where it decreases more slowly over hundreds of ms. Whether the resistance change is abrupt or gradual depends on the extent to which the EDL screens the electric field. Silver nanofilaments with gradual growth dynamics have potential application in neuromorphic computing. In this study, a linear (R2 > 0.9) dependence of conductance on the number of bias pulses is demonstrated—a signature feature that is required for neuromorphic application. Hundreds of distinguishable conductance states ranging from 235 to 260 microsimens can be accessed using a low read bias. These results show that novel PEGDA/IL composite electrolyte enables the gradual formation and dissolution of silver nanofilament with tunable conductance states, making it a promising candidate to advance neuromorphic applications

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Evolutionary computation based on nanocomposite training: application to data classification

Research into novel materials and computation frameworks by-passing the limitations of the current paradigm, has been identified as crucial for the development of the next generation of computing technology. Within this context, evolution in materio (EiM) proposes an approach where evolutionary algorithms (EAs) are used to explore and exploit the properties of un-configured materials until they reach a state where they can perform a computational task. Following an EiM approach, this thesis demonstrates the ability of EAs to evolve dynamic nanocomposites into data classifiers. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of configuration voltages creating an electric field applied to the material, and the infinite space of possible states the material can reach in response to this field. In a first set of investigations, two different algorithms, differential evolution (DE) and particle swarm optimisation (PSO), are used to evolve single-walled carbon nanotube (SWCNT) / liquid crystal (LC) composites capable of classifying artificial, two-dimensional, binary linear and non-linear separable and merged datasets at low SWCNT concentrations. The difference in search behaviour between the two algorithms is found to affect differently the composite’ state during training, which in turn affects the accuracy, consistency and generalisation of evolved solutions. SWCNT/LC processors are also able to scale to complex, real-life classification problems. Crucially, results suggest that problem complexity influences the properties of the processors. For more complex problems, networks of SWCNT structures tend to form within the composite, creating stable devices requiring no configuration voltages to classify data, and with computational capabilities that can be recovered more than several hours after training. A method of programming the dynamic composites is demonstrated, based on the reapplication of sequences of configuration voltages which have produced good quality SWCNT/LC classifiers. A second set of investigations aims at exploiting the properties presented by the dynamic nanocomposites, whilst also providing a means for evolved device encapsulation, making their use easier in out-of-the lab applications. Novel composites based on SWCNTs dispersed in one-part UV-cure epoxies are introduced. Results obtained with these composites support their choice for use in subsequent EiM research. A final discussion is concerned with evolving an electro-biological processor and a memristive processor. Overall, the work reported in the thesis suggests that dynamic nanocomposites present a number of unexpected, potentially attractive properties not found in other materials investigated in the context of EiM

New materials for electronics applications: nafion-gated nanowire field-effect transistors and metal-organic framework (MOF) single crystals

Nafion with its high ionic conductivity has been used as a proton conductor in proton exchange membrane fuel cells. In this study, we develop Nafion as an electronic material based on our finding that it works a negative tone resist in an electron beam lithography (EBL) process. X-ray photoelectron spectroscopy (XPS) studies show cleavage of ether groups in the side chain of Nafion, which causes the insolubility that provides contrast after EBL. There is also evidence that crosslinking between separate Nafion backbones contributes. We show that nanoscale Nafion patterned by EBL provides high performance as an ionic gating structure for n-InAs nanowire field-effect transistors. We also demonstrate the ability to make a complementary inverter by combining nanopatterned Nafion with n-InAs and p-GaAs field-effect transistors monolithically integrated on a common substrate. We used electrochemical impedance spectroscopy to characterize and better understand how the ionic conductivity of Nafion thin-films is affected by EBL exposure. We found that EBL causes an order of magnitude decrease in ionic conductivity for 230 nm thick films compared to only a 50% loss for 30 nm films. We characterized the water uptake of Nafion films using a Quartz Crystal Microbalance (QCM) technique. We found an approximately 30% decrease in the water uptake of 230 nm Nafion films and an approximately 50% increase for the thinner Nafion films. Preliminary neutron reflectometry results show that the water uptake of the 30 nm Nafion film is consistent with our QCM data. Since we can pattern nanoscale Nafion film using EBL, we attempted to use nanoscale EBL patterned Nafion film as an active layer in protonic field-effect transistors. Unfortunately, transistor behaviour could not be obtained, with further work needed to mitigate gate leakage issues. Metal-Organic Framework (MOF) materials with high electrical conductivity are of significant interest. Electrical characterization of these materials has been reported mostly for pressed pellets. In this thesis, we focused on measuring the electrical conductivity of single crystals of a newly-synthesised MOF, Cu(BTDAT)(MeOH). We found that the single crystals show high electrical conductivity, up to 40 uS/cm, at room temperature under ambient conditions with no observed anisotropy. We also performed electrical measurements at low temperatures between 83 K and 300 K. We found that current-voltage characteristics show good linearity at higher temperatures but become non-linear at lower temperatures. The conductivity decreases with reduced temperature. The fitting of an Arrhenius plot in the high-temperature range gave an activation energy Ea = 97 meV. We fabricated a field-effect transistor structure and found that the crystal did not exhibit any n-type or p-type semiconducting behaviour

A hybrid piezoelectric and electrostatic energy harvester for scavenging arterial pulsations

Implantable and wearable biomedical devices suffer from a limited lifespan of on-board batteries which results in a requirement to change the battery or the device itself causing additional physical discomfort. In order to overcome this, various energy harvesters have been developed. The human body possesses several types of energy available for scavenging through appropriately designed energy harvesting devices, while cardiovascular system in particular represents a constant reliable source of mechanical energy from vibration. Most conventional energy harvesters exploit only a single phenomenon, such piezo- or triboelectricity, thus producing reduced power density. As an improvement, hybridisation of energy harvesters intends to negate this drawback by simultaneously scavenging energy by multiple harvesters. In the present work, the reverse electrowetting on dielectric (REWOD) phenomenon is combined with the piezoelectric effect in a proof-of-concept hybrid harvester for scavenging biomechanical energy from arterial or other type pulsations. A mathematical model of the harvester was developed, and a computational investigation using CFD, and fluid-structure interaction simulations were carried out using the COMSOL Multiphysics software. The effect of the materials of piezoelectric film and geometrical features of the harvester on parameters such as the displacement, the frequency of pulsations and the energy produced were studied. An experimental setup that could imitate the displacements caused from arterial pulsations was designed and the produced electrical energy characteristics were analysed. A comparison between experimental and computational data was carried out and demonstrated a good agreement. Dependencies between geometrical parameters and electrical output were obtained, recommendation on piezoelectric materials and design solutions were provided

Computational Design of Nanomaterials

The development of materials with tailored functionalities and with continuously shrinking linear dimensions towards (and below) the nanoscale is not only going to revolutionize state of the art fabrication technologies, but also the computational methodologies used to model the materials properties. Specifically, atomistic methodologies are becoming increasingly relevant in the field of materials science as a fundamental tool in gaining understanding on as well as for pre-designing (in silico material design) the behavior of nanoscale materials in response to external stimuli. The major long-term goal of atomistic modelling is to obtain structure-function relationships at the nanoscale, i.e. to correlate a definite response of a given physical system with its specific atomic conformation and ultimately, with its chemical composition and electronic structure. This has clearly its pendant in the development of bottom-up fabrication technologies, which also require a detailed control and fine tuning of physical and chemical properties at sub-nanometer and nanometer length scales. The current work provides an overview of different applications of atomistic approaches to the study of nanoscale materials. We illustrate how the use of first-principle based electronic structure methodologies, quantum mechanical based molecular dynamics, and appropriate methods to model the electrical and thermal response of nanoscale materials, provides a solid starting point to shed light on the way such systems can be manipulated to control their electrical, mechanical, or thermal behavior. Thus, some typical topics addressed here include the interplay between mechanical and electronic degrees of freedom in carbon based nanoscale materials with potential relevance for designing nanoscale switches, thermoelectric properties at the single-molecule level and their control via specific chemical functionalization, and electrical and spin-dependent properties in biomaterials. We will further show how phenomenological models can be efficiently applied to get a first insight in the behavior of complex nanoscale systems, for which first principle electronic structure calculations become computationally expensive. This will become especially clear in the case of biomolecular systems and organic semiconductors