Volatile Memristors Research Articles

Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbOx volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor based neurons and nonvolatile TaOx memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems.

Spiking dynamic behaviors of NbO2 memristive neurons: A model study

Neuromorphic computing based on spikes has broad prospects in energy-efficient computation. Memristive neuron in this study is composed of two volatile memristors that have been shown to exhibit rich biological neuronal dynamics. Here, we show spiking dynamic behaviors of NbO2 memristive neurons by a detailed simulation study. With a DC input voltage, the operation windows of both periodic oscillation and neuron-like action potential spikes are recognized in the resistance–voltage phase diagrams of NbO2 memristive neurons. With a voltage pulse as the input, the periodic oscillation region can be classified into three subregions including the spike-OFF, spike-ON, and meta-spike transition regions. When the memristive neuron operates in the meta-spike transition region, it can regulate the “ON” and “OFF” states of the oscillation circuit by changing the ending time of the input pulse. It implies that both the input signal and the output signal determine the state of the circuit. The demonstration of a phase matching method provides a useful way for controlling “ON” and “OFF” states of the periodic oscillation behavior of the memristive neuron. Moreover, the effect of the circuit parameters on the peak-to-valley amplitude of the output spikes with action potential is investigated. A stable and controllable waveform output can be regulated by changing the capacitance, incorporating a series resistor, and customizing the active memristor. All these results provide a reliable reference for implementing memristive neurons in neuromorphic computing.

A Demonstration of Implication Logic Based on Volatile (Diffusive) Memristors

Implication logic gates that are based on volatile memristors are demonstrated experimentally with the use of relay-based volatile memristor emulators of an original design. The fabricated logic circuit involves two volatile memristors and it is capable of performing four fundamental logic functions (two types of material implication and the negations thereof). Moreover, current-voltage characteristics of individual emulators are recorded and self-sustained oscillations in a resistor-volatile memristor circuit are found. The developed emulator offers a great potential for memristive circuit experiments because of its simplicity, similarity of response with volatile memristors, and low cost. Our findings, which are based on emulators, can easily be reproduced with physical volatile memristors and, thus, open up possibilities for emerging in-memory computing architectures.

Morris-Lecar model of third-order barnacle muscle fiber is made of volatile memristors

Morris-Lecar model of third-order barnacle muscle fiber is made of volatile memristors

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

AbstractThreshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.

Computing with volatile memristors: an application of non-pinched hysteresis

The possibility of in-memory computing with volatile memristive devices, namely, memristors requiring a power source to sustain their memory, is demonstrated theoretically. We have adopted a hysteretic graphene-based field emission structure as a prototype of a volatile memristor, which is characterized by a non-pinched hysteresis loop. A memristive model of the structure is developed and used to simulate a polymorphic circuit implementing stateful logic gates, such as the material implication. Specific regions of parameter space realizing useful logic functions are identified. Our results are applicable to other realizations of volatile memory devices, such as certain NEMS switches.

Memristor models for machine learning.

In the quest for alternatives to traditional complementary metal-oxide-semiconductor, 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 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.

Gas Discharge Lamps Are Volatile Memristors

Discharge lamps can be classified as high-pressure and low-pressure lamps, which operate under different scientific principles. They have exhibited the well-known fingerprints of memristors. This paper describes the mathematical models of both of high- and low-pressure discharge lamps based on their respective physical nature and behaviors, and then explains how these models can be unified into a generalized mathematical framework that confirms their memristor characteristics. Practical and theoretical results from high-pressure and low-pressure lamps are included to illustrate their 3 fingerprints of the memristor characteristics. The results indicate that gas discharge lamps are not ideal but volatile memristors.