Memristive Structures Research Articles

Importance of growth method and substrate-induced crystalline quality in Al/Gd0.2Ca0.8MnO3/Au memristor devices

We report on the impact of the growth method and substrate-induced crystalline quality on the performance of planar Al/Gd0.2Ca0.8MnO3/Au memristor devices. Structural, magnetic, and resistive properties were thoroughly examined for fundamental characterizations, with a particular emphasis on their correlation with the memristive properties of fabricated devices. Our findings suggest that memristor structures grown on single crystal SrTiO3 substrates using pulsed laser deposition (PLD) consistently exhibit superior crystalline quality compared to those fabricated using chemical solution deposition and on silicon-based substrates. Despite variations in growth method and substrate, all memristor structures display typical resistive switching (RS) behaviour, distinguishing between high-resistance and low-resistance states. However, endurance and retention measurements demonstrate that memristor structures produced via PLD on single crystalline SrTiO3 demonstrate the most favourable RS properties. To elucidate the mechanisms underlying the differences in RS behavior across substrates and deposition methods, we extensively discuss these issues in the context of structural distortion and conduction mechanisms.

Research of Memristor Effect in Crossbar Architecture for Neuromorphic Artificial Intelligence Systems

This article presents the results of experimental studies of structures formed on the basis of the crossbar architecture of memristor structures made of various materials. TiO2 was used as a working memristor layer. The following materials were used for the contact pads: Al, Ni, Cr, Mo, Ta, Ag. In the course of experimental studies, the optimal combination of materials for the formation of crossbar memristor structures was revealed, which in the future can be used in devices of neuromorphic artificial intelligence systems.

Can We Still Find an Ideal Memristor?

In 1971, Chua defined an ideal memristor that links magnetic flux φ and electric charge q. In a magnetic lump with a current-carrying conductor, we found that the direct interaction between physical magnetic flux φ and physical electric charge q is memristive by nature in terms of a time-invariant φ-q curve being nonlinear, continuously differentiable and strictly monotonically increasing. Although we succeeded in demonstrating that the “ideal/real/perfect/… memristor” needs magnetism, the structure still suffers from two serious limitations: 1. a parasitic “inductor” effect and 2. bistability and dynamic sweep of a continuous resistance range. Then, we discussed how to overcome these two limitations to make a fully functioning ideal memristor with multiple or an infinite number of stable states and no parasitic inductance. We then gave a number of innovations to the current memristor structure, such as an “open” structure, nanoscale size, magnetic materials with cubic anisotropy (or even isotropy) and sequential switching of the magnetic domains. Contrary to the conjecture that “an ideal memristor may not exist or may be a purely mathematical concept”, we remain optimistic that an ideal memristor will be discovered in nature or will be made in the laboratory. Our finding of the memristive flux–charge interaction may advance the development and application of the memristor technology.

Principles, fabrication, and applications of halide perovskites‐based memristors

AbstractIn recent decades, the microelectronics industry has developed rapidly based on the von Neumann architecture and under the guidance of Moore's law. However, as the size of electronic devices approaches the limit and power consumption increases, traditional microelectronic materials and devices are facing more and more challenges. As a new type of semiconductor material, halide perovskites (HPs) have excellent photoelectric characteristics, such as high carrier mobility, controllable band structure, etc., which have been widely used in solar cells, light emitting diodes (LEDs), photodetectors, memristors, and in other fields. Among them, the memristor, as a new type of electronic device, is very promising for in‐memory computing with low power consumption by breaking the limit of von Neumann architecture. Especially, HPs‐based memristors show outstanding photoelectric response performance, low power consumption, and flexible wearability, allowing them to hold great application potential in logical operation, polymorphic storage, and neuromorphic computing, etc. In this review, we first briefly introduce the basic characteristics and preparation methods of HPs. Secondly, the development history, device structure, and performance parameters of memristors are depicted in detail. Thirdly, the resistance mechanism and application of HPs‐based memristors are discussed. Finally, the research status and development prospects of HPs‐based memristors are outlined.

Investigation of Plasticity in Memristive Structures Based on Nd2 – xCexCuO4 – y Epitaxial Films

Investigation of Plasticity in Memristive Structures Based on Nd2 – xCexCuO4 – y Epitaxial Films

Setting Plasma Immersion Ion Implantation of Ar+ Parameters towards Electroforming-Free and Self-Compliance HfO2-Based Memristive Structures.

Memristive structures are among the most promising options to be components of neuromorphic devices. However, the formation of HfO2-based devices in crossbar arrays requires considerable time since electroforming is a single stochastic operation. In this study, we investigate how Ar+ plasma immersion ion implantation (PI) affects the Pt/HfO2 (4 nm)/HfOXNY (3 nm)/TaN electroforming voltage. The advantage of PI is the simultaneous and uniform processing of the entire wafer. It is thought that Ar+ implantation causes defects to the oxide matrix, with the majority of the oxygen anions being shifted in the direction of the TaN electrode. We demonstrate that it is feasible to reduce the electroforming voltages from 7.1 V to values less than 3 V by carefully selecting the implantation energy. A considerable decrease in the electroforming voltage was achievable at an implantation energy that provided the dispersion of recoils over the whole thickness of the oxide without significantly affecting the HfOXNY/TaN interface. At the same time, Ar+ PI at higher and lower energies did not produce the same significant decrease in the electroforming voltage. It is also possible to obtain self-compliance of current in the structure during electroforming after PI with energy less than 2 keV.

Formation of BaTiO3 thin films for energy-efficient memristive applications

This paper shows the results of experimental studies of the influence of oxygen pressure under pulsed laser deposition on the electrophysical parameters of BaTiO3 nanocrystalline films. The elemental composition of BaTiO3 films obtained at different pressures (1×10−5 Torr (vacuum) and 1×10−2 Torr (oxygen)) was studied by XPS methods. Based on the obtained results, the energy-efficient memristor structures based on BaTiO3 nanocrystalline films were proposed. It was found that 20-nm-thick BaTiO3 films based memristive structure exhibited stable resistive switching effect: HRL/LRS∼10 for 100000 cycles. The results obtained can be used to fabricate energy-efficient memristive structures.

Photosensitive Memristor Structures Based on Reduced Graphene Oxide and Carbon Nanoparticles

Photosensitive Memristor Structures Based on Reduced Graphene Oxide and Carbon Nanoparticles

Parylene-MoOx crossbar memristors as a volatile reservoir and non-volatile readout: a homogeneous reservoir computing system.

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system. However, these parts require distinct behaviors found in differing memristive structures. The development of a homogeneous memristive reservoir computing system will significantly facilitate and reduce the fabrication process cost. The following work employs the co-existence of volatile and non-volatile regimes in parylene-MoOx crossbar memristors controlled by compliance current for this aim. The stable operation of the memristors under study is confirmed by low cycle-to-cycle and device-to-device variations of the switching voltages. For the transition between the volatile and non-volatile regimes, factors such as compliance current and reading voltage along with possible intrinsic origins are discussed. The results provide a foundation for the future hardware development of a homogeneous parylene-based reservoir computing system, considering high MNIST dataset classification accuracy (∼96%).

Effect of Nitrogen Ion Diffusion Jumps in Nanometer-Sized Si3N4 Memristors Investigated by Low-Frequency Noise Spectroscopy

The elementary jumps in the electron current through conducting filaments of two nanometer-sized virtual memristor structures consisting of a contact of a conductive atomic force microscope probe to the Si3N4 layer with the thickness of 6[Formula: see text]nm deposited onto the [Formula: see text]-Si(001) conductive substrates are investigated. These structures are: (S1) the Si3N4/Si film; (S2) the Si3N4/SiO2/Si stack, a similar structure with 2[Formula: see text]nm SiO2 sublayer between the film and the substrate. Usually, such investigations are performed by the analysis of the waveform of this current with the aim of extracting the random telegraph noise (RTN). Here, we develop a new indirect method, which is based on the measurement of the spectrum of the low-frequency flicker noise in the current without extracting the RTN. We propose that the flicker noise is caused by the motion (drift/diffusion) of nitrogen ions via vacancies within and around the filament. The number of these ions is estimated by taking into account the geometrical parameters of the filament. This allows us to estimate the root mean square magnitude [Formula: see text] of the current jumps, which are caused by random jumps of nitrogen ions, and the number M of these ions. This is fundamental for understanding the elementary mechanisms of the electron current flowing through the filament and the resistive switching in memristor devices.

A reservoir computing system with volatile and non-volatile organic memristors as a promising hardware architecture

In recent years, many scientific groups have been working on hardware implementation of the artificial neural networks to approach the computational efficiency of their biological counterpart. Memristors may play the role of synapses in such networks [1]. Varieties of memristive structures and materials have already been tested in different neural network architectures, but still no memristor is considered ideal for hardware synapse implementation [1]. One of the most significant problems is the presence of inherent stochasticity distinctive for all memristive devices, which complicates the training of the neural networks [1]. Several approaches were proposed to partially mitigate this problem, e.g., a reservoir computing system (RCS) [2] and spiking neural networks (SNN) [3] as well as defect engineering for memristive characteristics improvement. In this work, we propose to combine RCS with SNN and create a bio-inspired neuromorphic system based on two types of organic memristors with specifically designed structures and advanced characteristics. The RCS consists of two main parts: the reservoir and the readout [2]. The reservoir layer extracts some representative features from the input data due to its internal nonlinear dynamics. The readout layer then uses these features to classify the input data. Typically, a conventional fully connected neural network is used as a readout layer in the RCS. The training process occurs only in the readout layer, while a reservoir is not trainable. This decrease in trainable parameters considerably reduces the memristive stochasticity impact on the training process. The use of different types of memristors for the RCS is essential. The reservoir layer should consist of memristors with short-term memory, i.e., volatile memristors. This way, memristors can process each input sample individually. Volatile polyaniline-based memristors were chosen for this layer implementation. They can operate within a biologically plausible time range, which is essential as we aim to mimic biological systems [4]. In contrast, the reservoir layer should consist of memristors with long-term memory, i.e., non-volatile memristors, because the readout layer should preserve the trained synaptic weights. Non-volatile parylene memristors with incorporated MoO3 nanoparticles were chosen for the readout layer. The reservoir computing system adopts some essential principles of brain function, as both short- and long-term memory are significant in biological systems. However, traditional neural networks are commonly used as a readout layer in the RCSs [2]. Their training requires global weight updates, making them vulnerable to memristive stochasticity. In contrast, the SNNs allow local training, e.g., using bio-inspired learning rules, which makes them more effective and robust [3]. Consequently, we presume that a fully organic RCS with an SNN readout layer is a promising hardware memristive architecture. The work consists of two parts: hardware and software. First, the polyaniline- and parylene-based memristive devices were fabricated and tested. Hardware polyaniline reservoir demonstrated an ability to extract characteristic features from the input data. Nanocomposite parylene memristors were suitable for the role of synapses in the readout layer due to the unique combination of high switching speed, high stability, low power consumption and the possibility of crossbar implementation. Next, the traditional and spiking readout layers were compared in simulation. It was shown that the SNN readout layer is more adaptive and sustainable to noise in image classification tasks as well as memristive stochasticity [5].

Correlation between oxygen flow-controlled resistive switching and capacitance behavior in gallium oxide memristors grown via RF sputtering

We studied on the bipolar resistive switching (RS)-dependent capacitance of Ga2O3 memristors, grown using controlled oxygen flow via a radio frequency sputtering process. The Ag/Ga2O3/Pt memristor structure was employed to investigate the capacitance changes associated with RS behavior and oxygen concentration. In the low-resistance state (LRS), capacitance increased by over 60 times compared to the high-resistance state (HRS). Furthermore, in the HRS state, increasing the oxygen flow from 0 to 0.3 sccm resulted in an 80 % decrease in capacitance, while in the LRS state, capacitance increased by 128 %. These results indicate that RS-dependent capacitance in Ga2O3 memristors is influenced by the density of oxygen vacancies. The presence of oxygen vacancies affects charge storage capacity and capacitance, with higher oxygen concentrations leading to reduced capacitance in HRS and increased capacitance in LRS. The results contribute to the understanding of the capacitance behavior in Ga2O3 memristors and highlight the significance of oxygen vacancies in their operation.

Electron-Beam Deposition for the Synthesis of Memristive Structures Based on Hafnium Oxide

Electron-Beam Deposition for the Synthesis of Memristive Structures Based on Hafnium Oxide

Improved resistive switching performance of amorphous InGaZnO-based memristor with the TiO2 insertion layer

A stable I–V repeatability memristor based on amorphous InGaZnO (a-IGZO) with a TiO2 insertion layer was investigated. Two memristive structures composed of Pt/a-IGZO/Pt and Pt/TiO2/a-IGZO/Pt were manufactured on SiO2/Si substrate by the magnetron sputtering method. The crystallization of IGZO thin films was analyzed by the XRD process under different annealing temperatures, and the results show that the thin films remain in a stable amorphous state. The resistive switching performance of the two structures was tested by the semiconductor parameter analyzer 4200-SCS at room temperature. After 30 cyclic scans, the Pt/a-IGZO/Pt structure shows signs of degeneration in its I–V cyclic characteristics. However, in the Pt/TiO2/a-IGZO/Pt structure, the cyclic characteristics of I–V are highly stable (cycles >105 sweeps) with a limiting operating current of 0.01 A. Its rectification ratio is approximately 20 at low operating voltages (±1.0 V), and conductance increases (or decreases) progressively with continuous positive (or negative) voltage scans. Further analysis of the experimental energy band diagram was measured by UV–vis spectra and ultraviolet photoemission spectroscopy (UPS). The results demonstrate that the operational mechanism is the modulation of the barrier at the TiO2/a-IGZO interface, which is conducive to the stable I–V cyclic characteristic of Pt/TiO2/a-IGZO/Pt memristor.

Bi2O2Se-based CBRAM integrated artificial synapse

Integrating two-dimensional (2D) semiconducting materials into memristor structures has paved the way for emerging 2D materials to be employed in a vast field of memory applications. Bismuth oxyselenide (Bi2O2Se), a 2D material with high electron mobility, has attracted significant research interest owing to its great potential in various fields of advanced applications. Here, we explore the out-of-plane intrinsic switching behavior of few-layered Bi2O2Se via a cross point device for application in conductive bridge random access memory (CBRAM) and artificial synapses for neuromorphic computing. Via state-of-the-art methods, CVD-grown Bi2O2Se nanoplate is applied as a switching material (SM) in an Al/Cu/Bi2O2Se/Pd CBRAM structure. The device exhibits ∼90 consecutive DC cycles with a tight distribution of the SET/RESET voltages under a compliance current (CC) of 1 mA, a retention of over 10 ks, and multilevel switching characteristics showing four distinct states at Vread values of 0.1, 0.2, 0.25, and 0.3 V. Moreover, an artificial synapse is realized with potentiation and depression by modulating the conductance. The switching mechanism is explained via Cu migration through Bi2O2Se based on HRTEM analysis. The present structure shows potential for future integrated memory applications.

Local electrical characteristic of memristor structure in a high-resistance state obtained using electrostatic force microscopy: Fractal and multifractal dynamics of surface

A heterostructure BiFeO3/TiO2(Nt)Ti (BFOT) was obtained by the atomic layer deposition (ALD) method. After thermal treatment, the redistribution of Fe/Ti atoms forms an Aurivillius intermediate layered phase, and local charge capture centers are formed in the sample. Due to cationic non-stoichiometry, the BFO film exhibits p-type conductivity, while the nanotubes exhibit n-type conductivity due to oxygen vacancies. It was observed that lateral displacement of the sample can lead to ferroelectric switching, which can, in turn, affect the transition of the memristive structure from high-resistance (HRS) to low-resistance states (LRS). The hysteresis suppression tends to transition to an ohmic character and depends on the amplitude, frequency, and duration of the periodic signal. It has been found that compensation of static charge during resistive switching can affect the transport properties of the material. Fractal dimension analysis showed an acceleration of structure restructuring with increasing voltage, possibly contributing to the transition from an insulator to a metal in certain areas of the film volume. The joint analysis of piezoresponse force microscopy (PFM), electrostatic force microscopy (EFM), and fractal/multifractal dynamics showed a correlation between surface static charge and piezopotential. The new methodology described in this work can help understand the resistive switching processes in ferroelectric/semiconductor memristive structures.

QUANTIZATION OF ELECTRICAL CONDUCTANCE IN LAYERED Zr/ZrO<sub>2</sub>/Au MEMRISTIVE STRUCTURES

Anodic zirconia nanotubes are a promising functional medium for the formation of non-volatile resistive memory cells. The current-voltage characteristics in the region of low conductivity of the fabricated Zr/ZrO2/Au memristor structures have been studied in this work. For the first time, the reversible mechanisms of formation/destruction of single quantum conductors based on oxygen vacancies, which participate in processes of multiple resistive switching between low- and high-resistance states in a nanotubular dioxide layer, have been analyzed. An equivalent electrical circuit of a parallel resistor connection have been proposed and discussed to describe the observed memristive behavior of the studied layered structures.

Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing.

This review article attempts to provide a comprehensive review of the recent progress in cerium oxide (CeO2)-based resistive random-access memories (RRAMs). CeO2 is considered the most promising candidate because of its multiple oxidation states (Ce3+ and Ce4+), remarkable resistive-switching (RS) uniformity in DC mode, gradual resistance transition, cycling endurance, long data-retention period, and utilization of the RS mechanism as a dielectric layer, thereby exhibiting potential for neuromorphic computing. In this context, a detailed study of the filamentary mechanisms and their types is required. Accordingly, extensive studies on unipolar, bipolar, and threshold memristive behaviors are reviewed in this work. Furthermore, electrode-based (both symmetric and asymmetric) engineering is focused for the memristor's structures such as single-layer, bilayer (as an oxygen barrier layer), and doped switching-layer-based memristors have been proved to be unique CeO2-based synaptic devices. Hence, neuromorphic applications comprising spike-based learning processes, potentiation and depression characteristics, potentiation motion and synaptic weight decay process, short-term plasticity, and long-term plasticity are intensively studied. More recently, because learning based on Pavlov's dog experiment has been adopted as an advanced synoptic study, it is one of the primary topics of this review. Finally, CeO2-based memristors are considered promising compared to previously reported memristors for advanced synaptic study in the future, particularly by utilizing high-dielectric-constant oxide memristors.

Polysilicon-Channel Synaptic Transistors for Implementation of Short- and Long-Term Memory Characteristics.

The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler-Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms.

A Compact Fully Electronically Tunable Memristive Circuit Based on CCCDTA with Experimental Results.

This work presents a flux-controlled memristor structure employing a Current-Controlled Current Differencing Transconductance Amplifier (CCCDTA) with a grounded capacitor. The proposed emulator's invariant and variant parts can be safely adjustable, showing promising characteristics of up to 1.5 MHz operating frequency. Furthermore, there is no need for an additional circuit, switching mechanism or changing the circuit topology for the changing of operation modes. To justify the performance of the emulator with incremental and decremental mode operations, a Monte Carlo and temperature analysis are validated using TSMC 0.18 µm technology under a symmetrical supply voltage of ±0.9 V. Furthermore, the workability of the proposed circuit is tested with commercial elements such as ALD1116, AD844 and LM13700. When compared with other studies, the presented emulator circuit demonstrates promising performance in various features.