MnO/ZnO:Zn Thin-Film Frequency Adaptive Heterostructure for Future Sustainable Memristive Systems.

In recent years, advances in materials engineering based on adaptive electronics have found a new paradigm to optimize drawbacks in signal processing. A two-layer MnO/ZnO:Zn heterostructure envisioned for frequency adaptive electronic signal processing is synthesized by sputtering, where the use of internal states allows reconfigurability to obtain new operating modes at different frequency input signals. X-ray diffraction (XRD) analysis is performed on each layer, revealing a cubic structure for MnO and a hexagonal structure for ZnO:Zn with preferential growth in [111] and [002] directions, respectively. Scanning electron microscope (SEM) micrographs show that the surface of both materials is homogeneous and smooth. The thickness for each layer is determined to be approximately 106.3 nm for MnO, 119.3 nm for ZnO:Zn and 224.1 nm for the MnO/ZnO:Zn structure. An electrical characterisation with an oscilloscope and signal generator was carried out to obtain the time-response signals and current-voltage (I-V) curves, where no degradation is detected when changing frequencies within the range of 100 Hz to 1 MHz. An equivalent circuit is proposed to explain the effects in the interface. Measurements of switching speeds from high resistance state (HRS) to low resistance state (LRS) at approximately 17 ns, highlight the device's rapid adaptability, and an estimated switching ratio of approximately 2 × 104 indicates its efficiency as a memristive component. Finally, the MnO/ZnO:Zn heterojunction delivers states that are stable, repeatable, and reproducible, demonstrating how the interaction of the materials can be utilised in adaptive device applications by applying frequencies and internal states to create new and innovative design schematics, thus reducing the number of components/connections in a system for future sustainable electronics.

Random number generator based on a memristive circuit.

In this paper we discuss the details, limitations, and difficulties of the implementation in hardware of a memristor-based random number generator that exhibits monofractal/multifractal behavior. To do so, the components and selection criteria of a reference memristor and one proposed by the authors, the chaotic circuit leveraging them, and the processing that is performed on the chaotic signals to achieve the random discrete sequences are described. After applying the estimation tools, findings indicate that more than 60% of the proposed combinations allow generating random discrete sequences, with long-range dependence, and that both monofractal and multifractal behaviors can also be obtained. Consequently, a hardware system was achieved that can be used as a source of entropy in future synthetic biological signal generators.

A Star Network of Bipolar Memristive Devices Enables Sensing and Temporal Computing.

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and "rank-order" coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays.

Comparative Analysis of Reconfigurable Platforms for Memristor Emulation.

The memristor is the fourth fundamental element in the electronic circuit field, whose memory and resistance properties make it unique. Although there are no electronic solutions based on the memristor, interest in application development has increased significantly. Nevertheless, there are only numerical Matlab or Spice models that can be used for simulating memristor systems, and designing is limited to using memristor emulators only. A memristor emulator is an electronic circuit that mimics a memristor. In this way, a research approach is to build discrete-component emulators of memristors for its study without using the actual models. In this work, two reconfigurable hardware architectures have been proposed for use in the prototyping of a non-linearity memristor emulator: the FPAA (Field Programing Analog Arrays) and the FPGA (Field Programming Gate Array). The easy programming and reprogramming of the first architecture and the performance, high area density, and parallelism of the second one allow the implementation of this type of system. In addition, a detailed comparison is shown to underline the main differences between the two approaches. These platforms could be used in more complex analog and/or digital systems, such as neural networks, CNN, digital circuits, etc.

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior.

Resistive switching (RS) devices are promising forms of non-volatile memory. However, one of the biggest challenges for RS memory applications is the device-to-device (D2D) variability, which is related to the intrinsic stochastic formation and configuration of oxygen vacancy (VO) conductive filaments (CFs). In order to reduce the D2D variability, control over the formation and configuration of oxygen vacancies is paramount. In this study, we report on the Zr doping of TaOx-based RS devices prepared by pulsed-laser deposition as an efficient means of reducing the VOformation energy and increasing the confinement of CFs, thus reducing D2D variability. Our findings were supported by XPS, spectroscopic ellipsometry and electronic transport analysis. Zr-doped films showed increased VOconcentration and more localized VOs, due to the interaction with Zr. DC and pulse mode electrical characterization showed that the D2D variability was decreased by a factor of seven, the resistance window was doubled, and a more gradual and monotonic long-term potentiation/depression in pulse switching was achieved in forming-free Zr:TaOxdevices, thus displaying promising performance for artificial synapse applications.

On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits.

Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response requires a laborious fabrication process. Moreover, sample to sample variability makes experimentation with memristor-based synapses even harder. The usual alternatives are to either simulate or emulate the memristive systems under study. Both methodologies require the use of accurate modeling equations. In this paper, we present a diffusive compact model of memristive behavior that has already been experimentally validated. Furthermore, we implement an emulation architecture that enables us to freely explore the synapse-like characteristics of memristors. The main advantage of emulation over simulation is that the former allows us to work with real-world circuits. Our results can give some insight into the desirable characteristics of the memristors for neuromorphic applications.

ZnO-porous silicon nanocomposite for possible memristive device fabrication.

Preliminary results on the fabrication of a memristive device made of zinc oxide (ZnO) over a mesoporous silicon substrate have been reported. Porous silicon (PS) substrate is employed as a template to increase the formation of oxygen vacancies in the ZnO layer and promote suitable grain size conditions for memristance. Morphological and optical properties are investigated using scanning electron microscopy (SEM) and photoluminescence (PL) spectroscopy. The proposed device exhibits a zero-crossing pinched hysteresis current-voltage (I-V) curve characteristic of memristive systems.