Overwhelming coexistence of negative differential resistance effect and RRAM

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld's refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.

Low power highly flexible BiFeO3-based resistive random access memory (RRAM) with the coexistence of negative differential resistance (NDR)

We demonstrated the resistive random access memory characteristics for Cu (top contact)/BFO/PMMA (active layer)/ITO (bottom electrode)/PET sheet as a flexible substrate device configuration. The device showed non-volatile bipolar resistive switching characteristics with good repeatability and the coexistence of NDR for 100 cycles or more with 0.28/3.43 mW power consumption for 1^st/100^th cycles. The device retains its read state for 10⁴ s or more and switches from LRS to HRS or vice versa for 10³ cycles with a pulse width of 100 ms for a write-read-erase-read pulse without affecting the memory characteristics. The Weibull distribution suggests that a set state is more stable than the reset state with shape factor β = 25.20. The device follows Ohmic behavior for the lower applied external field and Child square and Schottky emission for the higher external fields. The Joule heating, Sorets, and Fick's forces are responsible for the formation and rupturing of ionic filament. The coexistence of resistive switching and flexible strength of the device sustains the bending curvature of infinity, 0.2 cm, 1 cm, 1.7 cm, and 2.2 cm. The memory characteristics are retained under tensile conditions for 100 cycles or more. More interestingly, the power consumption for sustaining the NDR region with bending (19 μW) is much lower than without bending (0.19 mW). Thus, this study provides the possibility of integrating BFO with flexible substrates suitable for hybrid organic/inorganic memory structures.

A magnetic field controlled memristor towards the design of an implantable detector

Memristors, which combine the behaviors of memory and resistive switching (RS), have a wide application prospect in information processing and artificial neural networks. The RS memory behaviors of memristors are primarily determined by the functional layer materials, device structure, and working conditions. Herein, a CuMnO₂ nanomaterial with the manganese copper ore structure was prepared on a Ti substrate by hydrothermal method, and a memristor with the Ag/CuMnO₂/Ti sandwich structure was developed. The RS memory behavior of the as-prepared memristor can be regulated through a low magnetic field (MF), and thus the resistance value of device shows a multi-level resistance states. Compared with other regulation factors, the MF can remotely adjust and control the RS characteristics of memristor, which is a non-invasive and non-destructive regulatory means. The MF regulated memristor can not only be used for multi-level high-density information storage, but also it can protect the health of special populations by identifying the MF intensity of the surrounding environment. When the device is operated in an MF environment, the change of resistance value of the device in both high resistance state (HRS) and low resistance state (LRS) is mainly attributed to the influence of Loren magnetic force on conductive ions.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO₄) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO₄ compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

Resistive Switching Crossbar Arrays Based on Layered Materials

Resistive switching (RS) devices are metal/insulator/metal cells that can change their electrical resistance when electrical stimuli are applied between the electrodes, and they can be used to store and compute data. Planar crossbar arrays of RS devices can offer a high integration density (>10⁸  devices mm^{- 2} ) and this can be further enhanced by stacking them three-dimensionally. The advantage of using layered materials (LMs) in RS devices compared to traditional phase-change materials and metal oxides is that their electrical properties can be adjusted with a higher precision. Here, the key figures-of-merit and procedures to implement LM-based RS devices are defined. LM-based RS devices fabricated using methods compatible with industry are identified and discussed. The focus is on small devices (size < 9 µm² ) arranged in crossbar structures, since larger devices may be affected by artifacts, such as grain boundaries and flake junctions. How to enhance device performance, so to accelerate the development of this technology, is also discussed.

Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse

Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of synaptic plasticity has shown promising results after the advent of memristors. However, neuronal intrinsic plasticity, which involves in learning process through interactions with synaptic plasticity, has been rarely demonstrated. Synaptic and intrinsic plasticity occur concomitantly in learning process, suggesting the need of the simultaneous implementation. Here, we report a neurosynaptic device that mimics synaptic and intrinsic plasticity concomitantly in a single cell. Threshold switch and phase change memory are merged in threshold switch-phase change memory device. Neuronal intrinsic plasticity is demonstrated based on bottom threshold switch layer, which resembles the modulation of firing frequency in biological neuron. Synaptic plasticity is also introduced through the nonvolatile switching of top phase change layer. Intrinsic and synaptic plasticity are simultaneously emulated in a single cell to establish the positive feedback between them. A positive feedback learning loop which mimics the retraining process in biological system is implemented in threshold switch-phase change memory array for accelerated training.

Synaptic plasticity and neuronal intrinsic plasticity are both involved in the learning process of hardware artificial neural network. Here, Lee et al. integrate a threshold switch and a phase change memory in a single device, which emulates biological synaptic and intrinsic plasticity simultaneously.

Vacuum and Low-Temperature Characteristics of Silicon Oxynitride-Based Bipolar RRAM

This study investigates the switching characteristics of the silicon oxynitride (SiOxNy)-based bipolar resistive random-access memory (RRAM) devices at different operating ambiances at temperatures ranging from 300 K to 77 K. The operating ambiances (open air or vacuum) and temperature affect the device’s performance. The electroforming-free multilevel bipolar Au/Ni/SiOxNy/p+-Si RRAM device (in open-air) becomes bilevel in a vacuum with an on/off ratio >104 and promising data retention properties. The device becomes more resistive with cryogenic temperatures. The experimental results indicate that the presence and absence of moisture (hydrogen and hydroxyl groups) in open air and vacuum, respectively, alter the elemental composition of the amorphous SiOxNy active layer and Ni/SiOxNy interface region. Consequently, this affects the overall device performance. Filament-type resistive switching and trap-controlled space charge limited conduction (SCLC) mechanisms in the bulk SiOxNy layer are confirmed.

A Neural Network Approach towards Generalized Resistive Switching Modelling

Resistive switching behaviour has been demonstrated to be a common characteristic to many materials. In this regard, research teams to date have produced a plethora of different devices exhibiting diverse behaviour, but when system design is considered, finding a 'one-model-fits-all' solution can be quite difficult, or even impossible. However, it is in the interest of the community to achieve more general modelling tools for design that allows a quick model update as devices evolve. Laying the grounds with such a principle, this paper presents an artificial neural network learning approach to resistive switching modelling. The efficacy of the method is demonstrated firstly with two simulated devices and secondly with a 4 μm2 amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 × 10-3 is achieved with a [2, 50,50 ,1] network structure, representing a good balance between model complexity and accuracy. A brief study on the number of hidden layers and neurons and its effect on network performance is also conducted with the best NRMSE reported at 4.63 × 10-3. The low error rate achieved in both simulated and real-world devices is a good indicator that the presented approach is flexible and can suit multiple device types.

From Memristive Materials to Neural Networks

The information technologies have been increasing exponentially following Moore's law over the past decades. This has fundamentally changed the ways of work and life. However, further improving data process efficiency is facing great challenges because of physical and architectural limitations. More powerful computational methodologies are crucial to fulfill the technology gap in the post-Moore's law period. The memristor exhibits promising prospects in information storage, high-performance computing, and artificial intelligence. Since the memristor was theoretically predicted by L. O. Chua in 1971 and experimentally confirmed by HP Laboratories in 2008, it has attracted great attention from worldwide researchers. The intrinsic properties of memristors, such as simple structure, low power consumption, compatibility with the complementary metal oxide-semiconductor (CMOS) process, and dual functionalities of the data storage and computation, demonstrate great prospects in many applications. In this review, we cover the memristor-relevant computing technologies, from basic materials to in-memory computing and future prospects. First, the materials and mechanisms in the memristor are discussed. Then, we present the development of the memristor in the domains of the synapse simulating, in-memory logic computing, deep neural networks (DNNs) and spiking neural networks (SNNs). Finally, the existent technology challenges and outlook of the state-of-art applications are proposed.

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

Negative differential resistance effect in resistive switching devices based on h-LuFeO3/CoFe2O4 heterojunctions

The negative differential resistance (NDR) effect enables multilevel storage and gradual resistance modulation in resistive switching (RS) devices to be achieved. However, the poor reproducibility of NDR is the obstacle that restricts their application because the appearance of the NDR effect in RS devices is usually accidental or unstable at room temperature. In this report, we demonstrate a polarization and interfacial defect modulated NDR effect in h-LuFeO₃/CoFe₂O₄ heterojunction-based RS devices; especially, the NDR is reproducible after hundreds of cycles at room temperature. This research provides an effective way for realizing the reproducible NDR effect in ferroelectric RS devices, and it may promote the development and application of RS devices with the NDR effect.

Multilevel resistive switching memory behaviors arising from ion diffusion and photoelectron transfer in α-Fe2O3 nano-island arrays

Resistive switching (RS) memory behaviors are observed in an Ag|α-Fe₂O₃|Ti device after operating under an ultralow bias voltage of ±0.1 V. An SET voltage of ∼20 mV is obtained under illumination. Multilevel RS memory is realized under photoelectric signal control. The separation and fast transfer of hole-electron pairs are responsible for the enhanced RS memory under illumination.

Physical mechanism for the synapse behaviour of WTiOx-based memristors

Tungsten-based memristors possess many advantages as candidates for memristive devices, including gradual changes in resistance states and memorization and learning functions. However, most previous reports mainly focus on studying synaptic learning rules instead of analysing the internal mechanism that results in the exterior learning rules. Herein, we discuss stacked Au/WTiO_x/Au and Ti/WTiO_x/Au devices in which the function of the resistance switch is realized by the externally induced local migration of oxygen ions. The consecutively adjustable multilevel resistance of the Au/WTiO_x/Au device may be due to the variation in the barrier width and height in high oxygen vacancy concentrations. Additionally, the high and low resistance states of Ti/WTiO_x/Au devices are considered as a result of the connection and rupture of the conductive filaments at low concentrations of oxygen vacancies. The physical mechanism construction and state-full synapse development through the control of ion migration provide insight into the applications of oxide-based memristors in neuromorphic computation.

Direct Observation of Structural Deformation Immunity for Understanding Oxygen Plasma Treatment-Enhanced Resistive Switching in HfOx-Based Memristive Devices

Oxygen ions' migration is the fundamental resistive switching (RS) mechanism of the binary metal oxides-based memristive devices, and recent studies have found that the RS performance can be enhanced through appropriate oxygen plasma treatment (OPT). However, the lack of experimental evidence observed directly from the microscopic level of materials and applicable understanding of how OPT improves the RS properties will cause significant difficulties in its further application. In this work, we apply scanning probe microscope (SPM)-based techniques to study the OPT-enhanced RS performance in prototypical HfOx based memristive devices through in situ morphology and electrical measurements. It is first found that the structural deformations in HfOx nanofilm induced by migration of oxygen ions and interfacial electrochemical reactions can be recovered by OPT effectively. More importantly, such structural deformations no longer occur after OPT due to the strengthening in lattice structure, which directly illustrates the enhanced quantity of HfOx nanofilm and the nature of enhanced RS properties after OPT. Finally, the underlying mechanisms of OPT-enhanced RS performance are analyzed by the results of X-ray photoelectron spectroscopic (XPS) surface analysis. In the OPT-enhanced HfOx nanofilm, oxygen vacancies in crystalline regions can be remarkably reduced by active oxygen ions' implantation. The oxygen ions transport will depend considerably on the grain boundaries and OPT-enhanced lattice structure will further guarantee the stability of conductive filaments, both of which ensure the uniformity and repeatability in RS processes. This study could provide a scientific basis for improving RS performance of oxides-based memristive devices by utilizing OPT.

Negative Differential Resistance Effect in Ru-Based RRAM Device Fabricated by Atomic Layer Deposition

In this work, Ru-based RRAM devices with atomic layer deposited AlO_y/HfO_x functional layer were fabricated and studied. A negative differential resistance (NDR) behavior was observed during the voltage set process, and its physical origin was explored. Based on the physics understanding of the resistive switching, the measured NDR behavior is believed to be associated with the partially unipolar reset effect, which is due to the recombination between oxygen vacancies and the thermally released oxygen ions from the RuO₂ interface layer. The measured electrical characteristics and X-ray photoelectron spectroscopy (XPS) results verified the physical interpretation.