Ferroelectric Second-Order Memristor

Antiferroelectric Heterostructures Memristors with Unique Resistive Switching Mechanisms and Properties

A novel antiferroelectric material, PbSnO3 (PSO), was introduced into a resistive random access memory (RRAM) to reveal its resistive switching (RS) properties. It exhibits outstanding electrical performance with a large memory window (>104), narrow switching voltage distribution (±2 V), and low power consumption. Using high-resolution transmission electron microscopy, we observed the antiferroelectric properties and remanent polarization of the PSO thin films. The in-plane shear strains in the monoclinic PSO layer are attributed to oxygen octahedral tilts, resulting in misfit dislocations and grain boundaries at the PSO/SRO interface. Furthermore, the incoherent grain boundaries between the orthorhombic and monoclinic phases are assumed to be the primary paths of Ag+ filaments. Therefore, the RS behavior is primarily dominated by antiferroelectric polarization and defect mechanisms for the PSO structures. The RS behavior of antiferroelectric heterostructures controlled by switching spontaneous polarization and strain, defects, and surface chemistry reactions can facilitate the development of new antiferroelectric device systems.

Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La0.1Bi0.9FeO3 Ferroelectric Tunnel Junctions

Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.

CMOS-compatible Hf0.5Zr0.5O2-based ferroelectric memory crosspoints fabricated with damascene process

We report the fabrication of Hf0.5Zr0.5O2(HZO) based ferroelectric memory crosspoints using a complementary metal-oxide-semiconductor-compatible damascene process. In this work, we compared 12 and 56µm2crosspoint devices with the 0.02 mm2round devices commonly used as a benchmark. For all devices, a 9 nm thick ferroelectric thin film was deposited by plasma-enhanced atomic layer deposition on planarized bottom electrodes. The wake-up appeared to be longer for the crosspoint memories compared to 0.02 mm2benchmark, while all the devices reached a 2Prvalue of ∼50µC cm-2after 105cycles with 3 V/10µs squared pulses. The crosspoints stand out for their superior endurance, which was increased by an order of magnitude. Nucleation limited switching experiments were performed, revealing a switching time <170 ns for our 12 and 56µm2devices, while it remained in theµs range for the larger round devices. The downscaled devices demonstrate notable advantages with a rise in endurance and switching speed.

An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.

Growth of emergent simple pseudo-binary ferroelectrics and their potential in neuromorphic computing devices

Ferroelectric memory devices such as ferroelectric memristors, ferroelectric tunnel junctions, and field-effect transistors are considered among the most promising candidates for neuromorphic computing devices. The promise arises from their defect-independent switching mechanism, low energy consumption and high power efficiency, and important properties being aimed for are reliable switching at high speed, excellent endurance, retention, and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. Binary or doped binary materials have emerged over conventional complex-composition ferroelectrics as an optimum solution, particularly in terms of CMOS compatibility. The current state-of-the-art route to achieving superlative ferroelectric performance of binary oxides is to induce ferroelectricity at the nanoscale, e.g., in ultra-thin films of doped HfO2, ZrO2, Zn1-xMgxO, Al-xScxN, and Bi1-xSmxO3. This short review article focuses on the materials science of emerging new ferroelectric materials, including their different properties such as remanent polarization, coercive field, endurance, etc. The potential of these materials is discussed for neuromorphic applications.

Physics, Structures, and Applications of Fluorite-Structured Ferroelectric Tunnel Junctions

The interest in ferroelectric tunnel junctions (FTJ) has been revitalized by the discovery of ferroelectricity in fluorite-structured oxides such as HfO2 and ZrO2 . In terms of thickness scaling, CMOS compatibility, and 3D integration, these fluorite-structured FTJs provide a number of benefits over conventional perovskite-based FTJs. Here, recent developments involving all FTJ devices with fluorite structures are examined. The transport mechanism of fluorite-structured FTJs is explored and contrasted with perovskite-based FTJs and other 2-terminal resistive switching devices starting with the operation principle and essential parameters of the tunneling electroresistance effect. The applications of FTJs, such as neuromorphic devices, logic-in-memory, and physically unclonable function, are then discussed, along with several structural approaches to fluorite-structure FTJs. Finally, the materials and device integration difficulties related to fluorite-structure FTJ devices are reviewed. The purpose of this review is to outline the theories, physics, fabrication processes, applications, and current difficulties associated with fluorite-structure FTJs while also describing potential future possibilities for optimization.

Quasi-Zero-Dimensional Ferroelectric Polarization Charges-Coupled Resistance Switching with High-Current Density in Ultrascaled Semiconductors

Ferroelectric memristors hold immense promise for advanced memory and neuromorphic computing. However, they face limitations due to low readout current density in conventional designs with low-conductive ferroelectric channels, especially at the nanoscale. Here, we report a ferroelectric-mediated memristor utilizing a 2D MoS2 nanoribbon channel with an ultrascaled cross-sectional area of <1000 nm2, defined by a ferroelectric BaTiO3 nanoribbon stacked on top. Strikingly, the Schottky barrier at the MoS2 contact can be effectively tuned by the charge transfers coupled with quasi-zero-dimensional polarization charges formed at the two ends of the nanoribbon, which results in distinctive resistance switching accompanied by multiple negative differential resistance showing the high-current density of >104 A/cm2. The associated space charges in BaTiO3 are minimized to ∼3.7% of the polarization charges, preserving nonvolatile polarization. This achievement establishes ferroelectric-mediated nanoscale semiconductor memristors with high readout current density as promising candidates for memory and highly energy-efficient in-memory computing applications.

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

Wake-Up Free Ultrathin Ferroelectric Hf0.5Zr0.5O2 Films

The development of the new generation of non-volatile high-density ferroelectric memory requires the utilization of ultrathin ferroelectric films. The most promising candidates are polycrystalline-doped HfO2 films because of their perfect compatibility with silicon technology and excellent ferroelectric properties. However, the remanent polarization of HfO2 films is known to degrade when their thickness is reduced to a few nanometers. One of the reasons for this phenomenon is the wake-up effect, which is more pronounced in the thinner the film. For the ultrathin HfO2 films, it can be so long-lasting that degradation occurs even before the wake-up procedure is completed. In this work, an approach to suppress the wake-up in ultrathin Hf0.5Zr0.5O2 films is elucidated. By engineering internal built-in fields in an as-prepared structure, a stable ferroelectricity without a wake-up effect is induced in 4.5 nm thick Hf0.5Zr0.5O2 film. By analysis of the functional characteristics of ferroelectric structures with a different pattern of internal built-in fields and their comparison with the results of in situ piezoresponse force microscopy and synchrotron X-ray micro-diffraction, the important role of built-in fields in ferroelectricity of ultrathin Hf0.5Zr0.5O2 films as well as the origin of stable ferroelectric properties is revealed.

Revival of Ferroelectric Memories Based on Emerging Fluorite-Structured Ferroelectrics

Over the last few decades, the research on ferroelectric memories has been limited due to their dimensional scalability and incompatibility with complementary metal-oxide-semiconductor (CMOS) technology. The discovery of ferroelectricity in fluorite-structured oxides revived interest in the research on ferroelectric memories, by inducing nanoscale nonvolatility in state-of-the-art gate insulators by minute doping and thermal treatment. The potential of this approach has been demonstrated by the fabrication of sub-30 nm electronic devices. Nonetheless, to realize practical applications, various technical limitations, such as insufficient reliability including endurance, retention, and imprint, as well as large device-to-device-variation, require urgent solutions. Furthermore, such limitations should be considered based on targeting devices as well as applications. Various types of ferroelectric memories including ferroelectric random-access-memory, ferroelectric field-effect-transistor, and ferroelectric tunnel junction should be considered for classical nonvolatile memories as well as emerging neuromorphic computing and processing-in-memory. Therefore, from the viewpoint of materials science, this review covers the recent research focusing on ferroelectric memories from the history of conventional approaches to future prospects.

Silicon based Bi0.9La0.1FeO3 ferroelectric tunnel junction memristor for convolutional neural network application

Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.

Ferroelectric Devices for Content-Addressable Memory

In-memory computing is an attractive solution for reducing power consumption and memory access latency cost by performing certain computations directly in memory without reading operands and sending them to arithmetic logic units. Content-addressable memory (CAM) is an ideal way to smooth out the distinction between storage and processing, since each memory cell is a processing unit. CAM compares the search input with a table of stored data and returns the matched data address. The issues of constructing binary and ternary content-addressable memory (CAM and TCAM) based on ferroelectric devices are considered. A review of ferroelectric materials and devices is carried out, including on ferroelectric transistors (FeFET), ferroelectric tunnel diodes (FTJ), and ferroelectric memristors.

Improved Performance of the Al2O3-Protected HfO2-TiO2 Base Layer with a Self-Assembled CH3NH3PbI3 Heterostructure for Extremely Low Operating Voltage and Stable Filament Formation in Nonvolatile Resistive Switching Memory

Herein, we report intriguing observations of an extremely stable nonvolatile bipolar resistive switching (NVBRS) memory device fabricated using HfO₂-TiO₂ topologically protected by Al₂O₃ as a stacked base layer for a CH₃NH₃PbI₃ (MAPI) electrolyte layer sandwiched between Ag and fluorine-doped tin oxide (FTO) electrodes. MAPI has been successfully synthesized by a rapid microwave-solvothermal (MW-ST) method within 10 min at 120 °C without requiring any inert gas atmosphere using low-cost precursors and solvents. Subsequently, MAPI powders are dissolved in aprotic solvents (DMF/DMSO = 8:2), and a spin-coated thin film is allowed to recrystallize upon annealing at 120 °C via a solution-based nanoscale self-assembly process. The fabricated memory device with the Ag/MAPI/Al₂O₃/TiO₂-HfO₂/FTO configuration shows an enhanced resistance ratio of 10⁵ for >10⁴ s at an extremely lower operating voltage (SET +0.2 V, RESET -0.2 V) when compared to that of the pristine MAPI device (±1 V, 10², 10⁴ s). We show that the memory device also exhibits a remarkable endurance of ≥3500 cycles due to the Al₂O₃ robust coating on the HfO₂-TiO₂ layer, facilitating prompt heterojunction formation. Thus, the adopted innovative strategies to prepare structurally and optically stable (∼1.5 years) MAPI under high-humid conditions could offer enhanced performance of NVBRS memory devices for medical, security, internet of things (IoT), and artificial intelligence (AI) applications.

Multiphotoconductance Levels of the Organic Semiconductor of Polyimide-Based Memristor Induced by Interface Charges

A memristor with Au/polyimide (PI)/Au structure is prepared by magnetron sputtering to investigate the multiphotoconductance resistive switching (RS) memory behavior. The PI-based memristor presents stable bipolar RS memory and is sensitive to visible light. Four discrete conductance states in both high-resistance state (HRS) and low-resistance state (LRS) are obtained when illuminating by 365, 550, 590, and 780 nm light. Electron trapping and detrapping from the defects distributed at interfaces and the PI switching layer are responsible for the observed RS memory behavior. The enhanced trapping and detrapping process by light illumination is responsible for the multiconductance states. This work provides the possibility for further development of neuromorphic vision sensors using an organic semiconductor-based memristor.

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.

Magnetoelectric Coupling at the Ni/Hf0.5Zr0.5O2 Interface

Composite multiferroics containing ferroelectric and ferromagnetic components often have much larger magnetoelectric coupling compared to their single-phase counterparts. Doped or alloyed HfO₂-based ferroelectrics may serve as a promising component in composite multiferroic structures potentially feasible for technological applications. Recently, a strong charge-mediated magnetoelectric coupling at the Ni/HfO₂ interface has been predicted using density functional theory calculations. Here, we report on the experimental evidence of such magnetoelectric coupling at the Ni/Hf_0.5Zr_0.5O₂(HZO) interface. Using a combination of operando XAS/XMCD and HAXPES/MCDAD techniques, we probe element-selectively the local magnetic properties at the Ni/HZO interface in functional Au/Co/Ni/HZO/W capacitors and demonstrate clear evidence of the ferroelectric polarization effect on the magnetic response of a nanometer-thick Ni marker layer. The observed magnetoelectric effect and the electronic band lineup of the Ni/HZO interface are interpreted based on the results of our theoretical modeling. It elucidates the critical role of an ultrathin NiO interlayer, which controls the sign of the magnetoelectric effect as well as provides a realistic band offset at the Ni/HZO interface, in agreement with the experiment. Our results hold promise for the use of ferroelectric HfO₂-based composite multiferroics for the design of multifunctional devices compatible with modern semiconductor technology.

Defects in ferroelectric HfO2

The discovery of ferroelectricity in polycrystalline thin films of doped HfO2 has reignited the expectations of developing competitive ferroelectric non-volatile memory devices. To date, it is widely accepted that the performance of HfO2-based ferroelectric devices during their life cycle is critically dependent on the presence of point defects as well as structural phase polymorphism, which mainly originates from defects either. The purpose of this review article is to overview the impact of defects in ferroelectric HfO2 on its functional properties and the resulting performance of memory devices. Starting from the brief summary of defects in classical perovskite ferroelectrics, we then introduce the known types of point defects in dielectric HfO2 thin films. Further, we discuss main analytical techniques used to characterize the concentration and distribution of defects in doped ferroelectric HfO2 thin films as well as at their interfaces with electrodes. The main part of the review is devoted to the recent experimental studies reporting the impact of defects in ferroelectric HfO2 structures on the performance of different memory devices. We end up with the summary and perspectives of HfO2-based ferroelectric competitive non-volatile memory devices.

Reversible oxygen migration and phase transitions in hafnia-based ferroelectric devices

Unconventional ferroelectricity exhibited by hafnia-based thin films-robust at nanoscale sizes-presents tremendous opportunities in nanoelectronics. However, the exact nature of polarization switching remains controversial. We investigated a La_0.67Sr_0.33MnO₃/Hf_0.5Zr_0.5O₂ capacitor interfaced with various top electrodes while performing in situ electrical biasing using atomic-resolution microscopy with direct oxygen imaging as well as with synchrotron nanobeam diffraction. When the top electrode is oxygen reactive, we observe reversible oxygen vacancy migration with electrodes as the source and sink of oxygen and the dielectric layer acting as a fast conduit at millisecond time scales. With nonreactive top electrodes and at longer time scales (seconds), the dielectric layer also acts as an oxygen source and sink. Our results show that ferroelectricity in hafnia-based thin films is unmistakably intertwined with oxygen voltammetry.

Effects of TiN Top Electrode Texturing on Ferroelectricity in Hf1-xZrxO2

Ferroelectric memories based on hafnium oxide are an attractive alternative to conventional memory technologies due to their scalability and energy efficiency. However, there are still many open questions regarding the optimal material stack and processing conditions for reliable device performance. Here, we report on the impact of the sputtering process conditions of the commonly used TiN top electrode on the ferroelectric properties of Hf_1-xZr_xO₂. By manipulating the deposition pressure and chemistry, we control the preferential orientation of the TiN grains between (111) and (002). We observe that (111) textured TiN is superior to (002) texturing for achieving high remanent polarization (P_r). Furthermore, we find that additional nitrogen supply during TiN deposition leads to >5× greater endurance, possibly by limiting the scavenging of oxygen from the Hf_1-xZr_xO₂ film. These results help explain the large P_r variation reported in the literature for Hf_1-xZr_xO₂/TiN and highlights the necessity of tuning the top electrode of the ferroelectric stack for successful device implementation.

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

A 2D-SnSe film with ferroelectricity and its bio-realistic synapse application

Catering to the general trend of artificial intelligence development, simulating humans' learning and thinking behavior has become the research focus. Second-order memristors, which are more analogous to biological synapses, are the most promising devices currently used in neuromorphic/brain-like computing. However, few second-order memristors based on two-dimensional (2D) materials have been reported, and the inherent bionic physics needs to be explored. In this work, a second-order memristor based on 2D SnSe films was fabricated by the pulsed laser deposition technique. The continuously adjustable conductance of Au/SnSe/NSTO structures was achieved by gradually switching the polarization of a ferroelectric SnSe layer. The experimental results show that the bio-synaptic functions, including spike-timing-dependent plasticity, short-term plasticity and long-term plasticity, can be simulated using this two-terminal devices. Moreover, stimulus pulses with nanosecond pulse duration were applied to the device to emulate rapid learning and long-term memory in the human brain. The observed memristive behavior is mainly attributed to the modulation of the width of the depletion layer and barrier height is affected, at the SnSe/NSTO interface, by the reversal of ferroelectric polarization of SnSe materials. The device energy consumption is as low as 66 fJ, being expected to be applied to miniaturized, high-density, low-power neuromorphic computing.

Bienenstock-Cooper-Munro Learning Rule Realized in Polysaccharide-Gated Synaptic Transistors with Tunable Threshold

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 10⁶, and a mobility of 9.5 cm² V^-1 s^-1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Hydrothermally Grown TiO2 Nanorod Array Memristors with Volatile States

In the present study, the memristive characteristics of hydrothermally grown TiO₂ nanorod arrays, particularly, the difference in the retention time of the resistance state, are investigated in dependence of the array growth temperature. A volatile behavior is observed and related to a redistribution of oxygen vacancies over time. It is shown that the retention time increases for increasing array growth temperatures from several seconds up to 20 min. The relaxation behavior is also seen in the current-voltage characteristics, which do not show the common unipolar, bipolar, or complementary switching behavior. Instead, the temporal evolution depends on the duration of the applied voltage and on the nanowire growth temperature. Therefore, electronic measurements are combined with scanning electron and scanning transmission electron microscopy, so that the amount of oxygen defect-rich grain boundaries in the upper part of the nanowires can be linked to the differences in the current-voltage behavior and retention time.

Memristor with a ferroelectric HfO2 layer: in which case it is a ferroelectric tunnel junction

New interest in the implementation of ferroelectric tunnel junctions has emerged following the discovery of ferroelectric properties in HfO2 films, which are fully compatible with silicon microelectronics technology. The coercive electric field to switch polarization direction in ferroelectric HfO2 is relatively high compared to classical perovskite materials, and thus it can cause the migration of non-ferroelectric charges in HfO2, namely charged oxygen vacancies. The charge redistribution would cause the change of the tunnel barrier shape and following change of the electroresistance effect. In the case of ambiguous ferroelectric properties of HfO2 ultrathin films, this oxygen-driven resistive switching effect can mimic the tunnel electroresistance effect. Here, we demonstrate two separate resistive switching regimes, depending on the applied voltage, in the same memristor device employing a ferroelectric Hf0.5Zr0.5O2 (4.5 nm) layer. The first regime originates from the polarization reversal, whereas the second one is attributed to the accumulation/depletion of the oxygen vacancies at the electrode interface. The modulation of the tunnel barrier causes the enhancement of R OFF/R ON ratio in ∼20 times compared to the tunnel electroresistance effect. The developed device was used to formulate the criteria for unambiguous discrimination between the ferroelectric-and non-ferroelectric resistive switching effects in HfO2-based ferroelectric tunnel junctions.