Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Asymptotic behavior of memristive circuits

The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.Comment: 20 pages, 8 figures; proofs corrected, figures changed; results substantially unchanged; to appear in Entrop

Dynamically altered conductance in an Organic Thin Film Memristive Device

The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first demonstration of the resistive switching effect, several types of memristive devices have been developed. In most of them, the memristive effect originates from direct modification of the conducting area, e.g. conducting filament formation/disintegration, or semiconductor doping/dedoping. Here, we report a solution-processed lateral memristive device based on a new conductivity modulation mechanism. The device architecture resembles that of an organic field-effect transistor in which the top gate electrode is replaced with an additional insulator layer containing mobile ions. Alteration of the ion distribution under the influence of applied potential changes the electric field, modifying the conductivity of the semiconductor channel. The devices exhibit highly stable current-voltage hysteresis loops and Short-Term Plasticity (STP). We also demonstrate short-term synaptic plasticity with tunable time constants

Projective Embedding of Dynamical Systems: uniform mean field equations

We study embeddings of continuous dynamical systems in larger dimensions via projector operators. We call this technique PEDS, projective embedding of dynamical systems, as the stable fixed point of the dynamics are recovered via projection from the higher dimensional space. In this paper we provide a general definition and prove that for a particular type of projector operator of rank-1, the uniform mean field projector, the equations of motion become a mean field approximation of the dynamical system. While in general the embedding depends on a specified variable ordering, the same is not true for the uniform mean field projector. In addition, we prove that the original stable fixed points remain stable fixed points of the dynamics, saddle points remain saddle, but unstable fixed points become saddles.Comment: 45 pages; one column; 10 figures

Stabilising Semiconducting Polymers Using Solid State Molecular Additives

Solution processed organic semiconductors contain extrinsic environmental species that cause device instabilities as they are difficult to remove during low temperature processing and are able to penetrate into organic electronics after fabrication. This dissertation is centered on the search for and development of solid state molecular additives to improve device stability associated with atmospheric defects in organic field effect transistors. To achieve this, an extended study was undertaken of the influence of over 95 different molecular additives expected to improve the stability characteristics of organic field effect transistors based on the literature. This dissertation demonstrates that positive bias and light stress stability can be improved in both p-type diF-TES ADT and IDT-BT organic field effect transistors by incorporating solid state small molecular additives. Simulations predict that the additives improve stability by introducing a competitive recombination pathway in order to prevent the trapping of electrons in the LUMO of the semiconductor by atmospheric species. Improvements with the solid state molecular additives are achieved by controlling the LUMO and morphology of the additive polymer blend. Secondly, improvements in the environmental and negative bias stress stability of organic field effect transistors are also observed with molecular additives. The solid state small molecular additives that significantly improve device characteristics are limited to a subset of molecules with similar structure to tetracyanoquinodimethane. It is demonstrated that this subset of solid state molecular additives correlates with a chemical reaction between the molecules and water. The chemical reaction appears to change the molecular additives into a new chemical species, plausibly consuming water, modifying the pH and doping the semiconductor, resulting in improved organic field effect transistor characteristics. Thirdly, machine learning techniques are used to accurately predict which solid state additives are capable of improving device performance. The machine learning algorithm uses neural passing networks for feature generation, due to its ability to capture physical plausible features such as functional groups. The algorithm screened over 1.5 billion molecular structures and found plausible molecular structures based on expert knowledge. Fourthly, novel analogue neuromorphic computer architectures based on anti-ferromagnetic and analogue transistors are modeled. The proposed architecture presents both trainable anti-ferromagnetic based synapses for learning and non-trainable voltage controlled synapses for computationally demanding inferences. This dissertation suggests that combining both the fully controllable and trainable networks is a promising route forward for analog neuromorphic computers.FlexEnable Christ's College Canadian Centennial Scholarship Fun

Reservoir Computing with Thin-film Ferromagnetic Devices

Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential for extreme parallelism and ultra-low power consumption. Physical reservoir computing demonstrates this with a variety of unconventional systems from optical-based to spintronic. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we show, through simulation, that magnetic materials in thin-film geometries can realise reservoir computers with greater than or similar accuracy to digital recurrent neural networks. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks. Furthermore, we show that neuromorphic hardware can be reduced in size by removing the need for discrete neural components and external processing. The natural dynamics and nanoscale size of magnetic thin-films present a new path towards fast energy-efficient computing with the potential to innovate portable smart devices, self driving vehicles, and robotics