Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

Trajectories entropy in dynamical graphs with memory

In this paper we investigate the application of non-local graph entropy to evolving and dynamical graphs. The measure is based upon the notion of Markov diffusion on a graph, and relies on the entropy applied to trajectories originating at a specific node. In particular, we study the model of reinforcement-decay graph dynamics, which leads to scale free graphs. We find that the node entropy characterizes the structure of the network in the two parameter phase-space describing the dynamical evolution of the weighted graph. We then apply an adapted version of the entropy measure to purely memristive circuits. We provide evidence that meanwhile in the case of DC voltage the entropy based on the forward probability is enough to characterize the graph properties, in the case of AC voltage generators one needs to consider both forward and backward based transition probabilities. We provide also evidence that the entropy highlights the self-organizing properties of memristive circuits, which re-organizes itself to satisfy the symmetries of the underlying graph.Comment: 15 pages one column, 10 figures; new analysis and memristor models added. Text improve

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

Memristives Schaltverhalten in selbst-assemblierten Nanopartikel-Systemen

In this work, the self-assembly of functional nanoparticle composites towards integration into future three-dimensional electronic circuitry was investigated. Using complementary surface-functionalization of metal and semiconductor nanoparticles, self-assembly of heterogeneous nanoparticle agglomerates in dispersion and the formation of nanoparticle arrays on oxide surfaces was shown. Electrical characterization of these systems yielded pronounced non-volatile bipolar memristive switching and threshold switching behavior, respectively.In dieser Arbeit wurde die Selbstassemblierung funktionaler Nanopartikelsysteme in Richtung der Integration in zukünftig dreidimensionale elektronische Schaltkreise untersucht. Durch komplementäre Oberflächenfunktionalisierung von Metall- und Halbleiternanopartikeln wurde die Selbstassemblierung von heterogenen Nanopartikel-Agglomeraten in Lösung und die regelmäßige Anordnung von Nanopartikeln auf Oxidoberflächen gezeigt. Die elektrische Charakterisierung dieser Systeme zeigte jeweils ausgeprägtes nicht-volatiles, bipolares memristives Schaltverhalten und Schwellspannungs-Schaltverhalten

Energetically deposited tin oxide: characterization and device applications

Semiconductor oxides are promising materials that have made impressive progress in recent years, challenging the dominance of silicon not only in conventional devices including field-effect transistors but being amenable to next-generation electronic devices such as memristors. Although a variety of oxides have been explored, tin oxide has been an interesting material for researchers when offering p-type characteristics of tin monoxide SnO and n-type characteristics in tin dioxide SnO2. While SnO2 is easy to grow and well suited for a wide range of applications, it is difficult to form p-type SnO due to its metastability where it forms into the more stable phase SnO2. The work presented in this Doctoral Dissertation focus on exploring the characteristics and applications of energetically deposited tin oxide thin films. The tin oxide film deposited using high-power impulse magnetron sputtering was found to be mixed-phase nanocrystalline SnO and SnO2 in which SnO2 is dominant. The high resistivity, low carrier concentration and low mobility in the as-deposited and annealed samples hindered the application of the high-power impulse magnetron sputtering (HiPIMS) SnOx in thin film transistors, however, suggested suitability for these films as a memristive material. A small but quantifiable variation in film stoichiometry (Sn:O) resulting from the off-axis deposition led to the formation of two different types of memristive devices, namely filamentary and nanoparticle network memristors. Both devices exhibited stable volatile bidirectional resistive switching with a ratio between high resistance and low resistance of more than two orders of magnitude. However, their underlying resistive switching mechanisms and device characteristics were significantly different. Synaptic-like behaviours were observed on both filamentary devices (FDs) and nanoparticle network devices (NNDs), highlighting their potential for information processing in neuromorphic computing systems. While a FD can become only an individual cell in reservoir computing circuits, an NND can be implemented as a reservoir due to their available inter-connectivity which is required for reservoir computing