Defect-Engineered Electroforming-Free Analog HfOx Memristor and Its Application to the Neural Network

Heterogeneous density-based clustering with a dual-functional memristive array

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Memristive Explainable Artificial Intelligence Hardware

Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision-making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides an explanation and interpretation of its decisions, thereby promoting the trustworthiness of AI systems. Here, a memristive XAI hardware framework is presented. This framework incorporates three distinct types of memristors (Mott memristor, valence change memristor, and charge trap memristor), each responsible for performing three essential functions (perturbation, analog multiplication, and integration) required for the XAI hardware implementation. Three memristor arrays with high robustness are fabricated and the image recognition of 3 × 3 testing patterns and their explanation map generation are experimentally demonstrated. Then, a software-based extended system based on the characteristics of this hardware is built, simulating a large-scale image recognition task. The proposed system can perform the XAI operations with only 4.32% of the energy compared to conventional digital systems, enlightening its strong potential for the XAI accelerator.

Multiphase Reset Induced Reliable Dual-Mode Resistance Switching of the Ta/HfO2/RuO2 Memristor

Higher functionality should be achieved within the device-level switching characteristics to secure the operational possibility of mixed-signal data processing within a memristive crossbar array. This work investigated electroforming-free Ta/HfO2/RuO2 resistive switching devices for digital- and analog-type applications through various structural and electrical analyses. The multiphase reset behavior, induced by the conducting filament modulation and oxygen vacancy generation (annihilation) in the HfO2 layer by interacting with the Ta (RuO2) electrode, was utilized for the switching mode change. Therefore, a single device can manifest stable binary switching between low and high resistance states for the digital mode and the precise 8-bit conductance modulation (256 resistance values) via an optimized pulse application for the analog mode. An in-depth analysis of the operation in different modes and comparing memristors with different electrode structures validate the proposed mechanism. The Ta/HfO2/RuO2 resistive switching device is feasible for a mixed-signal processable memristive array.

Effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices: the role of ZnO grain boundaries

The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 °C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 °C to 700 °C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 °C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution σ/μ, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.

Deterministic Conductive Filament Formation and Evolution for Improved Switching Uniformity in Embedded Metal-Oxide-Based Memristors─A Phase-Field Study

The extreme device-to-device variation of switching performance is one of the major obstacles preventing the applications of metal-oxide-based memristors in large-scale memory storage and resistive neural networks. Recent experimental works have reported that embedding metal nano-islands (NIs) in metal oxides can effectively improve the uniformity of the memristors, but the underlying role of the NIs is not fully understood. Here, to address this specific problem, we develop a physical model to understand the origin of the variability and how the embedded NIs can improve the performance and uniformity of memristors. We find that due to the dimension confinement effect, embedding metal NIs can modulate the electric field distribution and lead to a more deterministic formation of the conductive filament (CF) from their vicinity, in contrast to the random growth of CFs without embedded NIs. This deterministic CF formation, via vacancy nucleation, further reduces the forming, reset, and set voltages and enhances the uniformity of the operation voltages and current ON/OFF ratios. We further demonstrate that modifying the shapes of the metal NIs can modulate the field strengths/distributions around the NIs and that choosing the NI metal composition and shape that chemically facilitate vacancy formations can further optimize the CF morphology, reduce the operation voltages, and improve the switching performance. Our work thus provides a fundamental understanding of how embedded metal NIs improve the resistive switching performance in oxide-based memristors and could potentially guide the selection of embedded NIs to realize a more uniform memristor that also operates at a higher efficiency than present materials.

Evaluating charge-type of polyelectrolyte as dielectric layer in memristor and synapse emulation

Based on credible advantages, organic materials have received more and more attention in memristor and synapse emulation. In particular, an implementation of the ionic pathway as a dielectric layer is essential for organic materials used as building blocks of memristor and artificial synaptic devices. Herein, we describe an evaluation of the use of positive and negative polyelectrolytes as dielectric layers for a memristor with calcium ion (Ca²⁺) doping. The device based on a negative polyelectrolyte shows the potential to obtain an excellent resistive switching performance and synapse functionality, especially in the transformation behaviours from short-term plasticity (STP) to long-term plasticity (LTP) in both the potentiation and depression processes, which were comparable to the perfomrmance obtained with a positive polyelectrolyte. The mechanism of electrical resistance transition and synaptic function can be attributed to the migration of the doped Ca²⁺ and the ionic functional groups of polyelectrolyte, which result in the formation and vanishing filament-like Ca²⁺ flux.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta₂ O₅ /Nb₂ O_{5- x} /Al₂ O_{3- y} /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 10⁵ s at 150 °C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Modified Dynamic Physical Model of Valence Change Mechanism Memristors

Valence change-type resistance switching behaviors in oxides can be understood by well-established physical models describing the field-driven oxygen vacancy distribution change. In those models, electroformed residual oxygen vacancy filaments are crucial as they work as an electric field concentrator and limit the oxygen vacancy movement along the vertical direction. Therefore, their movement outward by diffusion is negligible. However, this situation may not be applicable in the electroforming-free system, where the field-driven movement is less prominent, and the isotropic oxygen vacancy diffusion by concentration gradient is more significant, which has not been given much consideration in the conventional model. Here, we propose a modified physical model that considers the change in the oxygen vacancies' charged state depending on their concentrations and the resulting change in diffusivity during switching to interpret the electroforming-free device behaviors. The model suggests formation of an hourglass-shaped filament constituting a lower concentration of oxygen vacancies due to the fluid oxygen diffusion in the thin oxide. Consequently, the proposed model can explain the electroforming-free device behaviors, including the retention failure mechanism, and suggest an optimized filament configuration for improved retention characteristics. The proposed model can plausibly explain both the electroformed and the electroforming-free devices. Therefore, it can be a standard model for valence change memristors.

Multifunctional Analog Resistance Switching of Si3N4-Based Memristors through Migration of Ag+ Ions and Formation of Si-Dangling Bonds

With forming-free, self-rectifying, and self-compliant properties, memristors can effectively prevent themselves from experiencing leakage currents and overshoot voltages without any additional circuitry. However, the implementation of all these features in a single memristor remains a challenge. Herein, a multifunctional Si3N4-based memristor with a structure of Ag/a-SiNx/p++-Si has been fabricated, and it was demonstrated, for the first time, that the device exhibits novel analog resistance switching behaviors, such as being forming-free, self-rectifying, and self-compliant, presenting well a coexistence of volatile and nonvolatile performance of resistance switching. The multifunctional analog resistance switching could be attributed to the formation of the Si-dangling bond channel and the migration of Ag+ ions inside the a-SiNx layer. Our current results might provide an insightful understanding of the resistance switching mechanism of Si3N4-based memristors, and the device with a large on/off ratio (>103) and robust retention (>103 s) and endurance (>103 cycles) shows potential for application in crossbar synaptic array devices.

Composite Memristors by Nanoscale Modification of Hf/Ta Anodic Oxides

Composite memristors based on anodic oxidation of Hf superimposed on Ta thin films are studied. A layered structure is obtained by successive sputtering of Ta and Hf thin films. The deposition geometry ensured components' thickness gradient profiles (wedges) aligned in opposite directions. Anodization in citrate buffer electrolyte leads to a nanoscale columnar structuring of Ta₂O₅ in HfO₂ due to the higher electrical resistance of the latter. Following the less resistive path, the ionic current forces Ta oxide to locally grow toward the electrolyte interface according to the Rayleigh-Taylor principle. The obtained composite oxide memristive properties are studied as a function of the Hf/Ta thickness ratio. One pronounced zone prominent for memristive applications is found for ratios between 4 and 5. Here, unipolar and bipolar memristors are found, with remarkable endurance and retention capabilities. This is discussed in the frame of conductive filament formation preferentially along the interfaces between oxides.

Area-Type Electronic Bipolar Switching Al/TiO1.7/TiO2/Al Memory with Linear Potentiation and Depression Characteristics

In electronic bipolar resistive switching (eBRS), the electron trapping and detrapping at the defect sites within the switching layer, such as the highly defective TiO_1.7 in this study, constitute the switching mechanism. It is an appealing candidate solution to the nonuniformity issue of resistive switching memory. However, TiO_1.7-based eBRS has suffered from a lack of endurance and retention. In this study, a 7 nm-thick stoichiometric TiO₂ layer is interposed between an Al bottom electrode and a 50 nm-thick TiO_1.7 layer, which is in contact with an Al top electrode. Despite the minimal structural modification, improvements in the electrical performance were substantial. The off-to-on state resistance ratio of 20 and the resistance values could be retained up to 30 000 direct current sweep cycles and 10⁶ alternating current pulse switching cycles. Data retention also significantly improves. Moreover, the device is electroforming-free and shows fully area-type switching characteristics. Such notable improvements are attributed to the favorable energy band structure of the Al/TiO_1.7/TiO₂/Al structure. The device shows almost linear potentiation and depression characteristics after the repeated pulse voltage applications, which significantly improves the accuracy of the neural network, the synapses of which are composed of the Al/TiO_1.7/TiO₂/Al memory cells.

Flexible Ta/TiOx/TaOx/Ru memristive synaptic devices on polyimide substrates

It is very urgent to build memristive synapses and even wearable devices to simulate the basic functions of biological synapses. The linear conductance modulation is the basis of analog memristor for neuromorphic computing. By optimizing the interface engineering wherein Ta/TiO_x/TaO_x/Ru was fabricated, all the memristor devices with different TiO_xthickness showed electroforming-free property. The short-term and long-term plasticity in both potentiation and depression behaviors can be mimicked when TiO_xwas fixed at 25 nm. The presented memristive synapses simulated the stable paired-pulse facilitation and spike-timing dependent plasticity performance. The potentiation and depression in linearity and symmetry improved with the TiO_xthickness increasing, which provides the feasibility for the application of artificial neural network. In addition, the device deposited on polyimide (PI) still exhibits the synaptic performance until the bending radii reaches 6 mm. By carefully tuning the interface engineering, this study can provide general revelation for continuous improvement of the memristive performance in neuromorphic applications.

Learning to Approximate Functions Using Nb-Doped SrTiO3 Memristors

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.

Complementary Resistive Switching and Synaptic-Like Memory Behavior in an Epitaxial SrFeO2.5 Thin Film through Oriented Oxygen-Vacancy Channels

Oxygen-vacancy-ordered brownmillerite oxides offer a reversible topotactic phase transition by significantly varying the oxygen stoichiometry of the material without losing its lattice framework. This phase transition leads to substantial changes in the physical and chemical properties of brownmillerite oxides, including electrical and ion conductivity, magnetic state, and oxygen diffusivity. In this study, the variations in the resistive switching mode of the epitaxial brownmillerite SrFeO_2.5 thin film in the device were studied by systematically controlling the oxygen concentration, which could be varied by changing the compliance current during the first electroforming step. Depending on the compliance current, the SrFeO_2.5 devices exhibited either low-power bipolar resistive switching or complementary resistive switching behaviors. A physical model based on the internal redistribution of oxygen ions between the interfaces with the top and the bottom electrodes was developed to explain the complementary resistive switching behavior. This model was experimentally validated using impedance spectroscopy. Finally, the gradual conductance variation in the brownmillerite SrFeO_2.5 thin films was exploited to realize synaptic learning.