A mathematical framework for the analysis and modelling of memristor nanodevices

This work presents a set of mathematical tools for the analysis and modelling of memristor devices. The mathematical framework takes advantage of the compliance of the memristor's output dynamics with the family of Bernoulli differential equations which can always be linearised under an appropriate transformation. Based on this property, a set of conditionally solvable general solutions are defined for obtaining analytically the output for all possible types of ideal memristors. To demonstrate its usefulness, the framework is applied on HP's memristor model for obtaining analytical expressions describing its output for a set of different input signals. It is shown that the output expressions can lead to the identification of a parameter which represents the collective effect of all the model's parameters on the nonlinearity of the memristor's response. The corresponding conclusions are presented for series and parallel networks of memristors as well. The analytic output expressions enable also the study of several device properties of memristors. In particular, the hysteresis of the current-voltage response and the harmonic distortion introduced by the device are investigated and both interlinked with the nonlinearity of the system. Moreover, the reciprocity principle, a property form classical circuit theory, is shown to hold for ideal memristors under specific conditions. Based on the insights gained through the analysis of the ideal element, this work takes a step further into the modelling of memristive devices in an effort to improve some of the macroscopic models currently used. In particular, a method is proposed for extracting the window function directly from experimentally acquired input-output measurements. The method is based on a simple mathematical transformation which relates window to sigmoidal functions and a set of assumptions which allow the mapping of the sigmoidal to current-voltage measurements. The equivalence between the two representations is demonstrated through a new generalised window function and several existing sigmoidals and windows. The proposed method is applied on three sets of experimental measurements which demonstrate the usefulness of the window modelling approach and the newly proposed window function. Based on this method the extracted windows are tailored to the device under investigation. The analysis also reveals a set of non-idealities which lead to the introduction of a new model for memristive devices whose response cannot be captured by the window-based approach.Open Acces

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Optically-Triggered Nanoscale Memory Effect in a Hybrid Plasmonic-Phase Changing Nanostructure

Nanoscale devices, such as all-optical modulators and electro-optical transducers, can be implemented in heterostructures that integrate plasmonic nanostructures with functional active materials. Here we demonstrate all-optical control of a nanoscale memory effect in such a heterostructure by coupling the localized surface plasmon resonance (LSPR) of gold nanodisk arrays to a phase-changing material (PCM), vanadium dioxide (VO<inf>2</inf>). By latching the VO<inf>2</inf> in a distinct correlated metallic state during the insulator-to-metal transition (IMT), while concurrently exciting the hybrid nanostructure with one or more ultraviolet optical pulses, the entire phase space of this correlated state can be accessed optically to modulate the plasmon response. We find that the LSPR modulation depends strongly but linearly on the initial latched state, suggesting that the memory effect encoded in the plasmon resonance wavelength is linked to the strongly correlated electron states of the VO<inf>2</inf>. The continuous, linear variation of the electronic and optical properties of these model heterostructures opens the way to multiple design strategies for hybrid devices with novel optoelectronic functionalities, which can be controlled by an applied electric or optical field, strain, injected charge, or temperature.Department of Applied Physic

Memristor devices based on low-bandwidth manganites

This dissertation investigates the phenomenon of resistive switching (RS) in lowbandwidth mixed-valence perovskite manganite oxides. In particular, the compounds Pr0.6Ca0.4MnO3 and Gd1−xCaxMnO3 with x between 0 and 1 are studied. The steps of sample fabrication, crystalline properties and measurements to verify the quality of the devices are also reported. The thin film memristor devices were fabricated from target pellets using pulsed laser deposition on single crystal SrTiO3 substrates. The crystallinity was verified using X-ray diffraction and the elemental composition by energy dispersive X-ray spectroscopy. The fabricated thin films were used to create memristor devices by depositing patterned metal electrodes on them by either DC magnetron sputtering or e-beam physical vapor deposition. When the studied materials were combined with a reactive electrode material, the formed interface exhibited the phenomenon of resistive switching, where the resistance of the device can be modified non-volatilely by application of electric field to the terminals of the device. The noble metals Au and Ag were found to be optimal for the passive interfaces, and Al as the active interface. The RS properties of the devices made with the optimal electrode configuration were studied in detail. The devices were found to have asymmetric bipolar RS with promising characteristics. The studies encompassed varying the calcium doping of the samples, studying the endurance and timing characteristics of the RS phenomenon as well as measuring the device characteristics as a function of temperature. The RS properties were found to vary significantly over the calcium doping range. When the measurement results were used in a conduction model analysis, the switching properties were found to be correlated with the trap-energy level of the Al/GCMOinterface region. Lastly, the GCMO memristor devices were modeled successfully using a compact model compatible with circuit simulators and the biologicallyinspired spike-timing-dependent plasticity learning rule was demonstrated. In conclusion, GCMO is a promising new material for RS-based neuromorphic applications due to its stable switching properties. The unexpected differences between GCMO and PCMO show that there are still many unexplored RS properties and behaviors within the manganite family that can be explored in future research