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

Volatile Resistive Switching and Short-Term Synaptic Plasticity in a Ferroelectric-Modulated SrFeOx Memristor

SrFeOx (SFO) offers a topotactic phase transformation between an insulating brownmillerite SrFeO2.5 (BM-SFO) phase and a conductive perovskite SrFeO3 (PV-SFO) phase, making it a competitive candidate for use in resistive memory and neuromorphic computing. However, most of existing SFO-based memristors are nonvolatile devices which struggle to achieve short-term synaptic plasticity (STP). To address this issue and realize STP, we propose to leverage ferroelectric polarization to effectively draw ions across the interface so that the PV-SFO conductive filaments (CFs) can be ruptured in absence of an external field. As a proof of concept, we fabricate ferroelectric Pb(Zr0.2Ti0.8)O3 (PZT)/BM-SFO bilayer films with Au top electrodes and SrRuO3 bottom electrodes. The device exhibits the desired volatile resistive switching behavior, with its low resistance state decaying over time. Such volatility is attributed to the positive polarization charge near the PZT/SFO interface, which can attract the oxygen ions from SFO to PZT and hence lead to the rupture of CFs. Moreover, this volatile device successfully emulates STP-related synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, learning-experience behavior, associative learning, and reservoir computing. Our study showcases an effective method for achieving volatile resistive switching and STP, which may be applied to various systems beyond SFO-based memristors.

Inflection of Resistive Switching Behavior in Atomically Precise Silver Cluster-Assembled Materials

Bottom-up design of electronic materials based on nanometer-sized building blocks requires precise control over their self-assembly process. Atomically precise metal nanoclusters (NCs) are the well-characterized building blocks for crafting tunable nanomaterials. Here, a solution-processed assembly of a newly synthesized molecular silver nanocluster (0 D Ag12-NC) into a 1D nanocluster chain (1 D Ag12-CAM) is mediated by 4,4'-bipyridine linker Both 0 D Ag12-NC and 1 D Ag12-CAM consist of Ag12 core that adopts the cuboctahedron geometry protected by organic ligands. The resistive switching devices are fabricated in a sandwich-like structure of ITO (Indium tin oxide)/X/Ag (where X is either 0 D Ag12-NC or 1 D Ag12-CAM). The device based on 1 D Ag12-CAM exhibited excellent resistive switching behaviour, enduring up to 1000 cycles and boasting a fivefold higher Ion/Ioff ratio compared to the device based on the molecular 0 D Ag12-NC nanocluster. Furthermore, the device based on 1 D Ag12-CAM demonstrated negative differential resistance (NDR) phenomena, achieving a peak-to-valley ratio of 2.34 with a switching efficiency of 23 Ns. This work highlights the importance of interconnecting molecular nanoclusters into 1D nanocluster chains for fine-tuning resistive memory properties in futuristic electronic appliances.

Oxygen Vacancy Compensation-Induced Analog Resistive Switching in the SrFeO3-δ/Nb:SrTiO3 Epitaxial Heterojunction for Noise-Tolerant High-Precision Image Recognition

Neuromorphic computing, inspired by the brain's architecture, promises to surpass the limitations of von Neumann computing. In this paradigm, synaptic devices play a crucial role, with resistive switching memory (memristors) emerging as promising candidates due to their low power consumption and scalability advantages. This study focuses on the development of metal/oxide-semiconductor heterojunctions, which offer several technological advantages and have broad potential for applications in artificial neural synapses. However, constructing high-quality epitaxial interfaces between metal and oxide semiconductors and designing modifiable contact barriers are challenging. Herein, we construct high-quality epitaxial metal/semiconductor interfaces based on the metallicity of the perovskite phase SrFeO3-δ (PV-SFO) and a small Schottky barrier in contact with Nb-doped SrTiO3 (NSTO). X-ray diffraction patterns, reciprocal space mapping results, and cross-sectional transmission electron microscopy images reveal that the prepared PV-SFO film exhibits a perfect single-crystal structure and an excellent epitaxial interface with the NSTO (111) substrate. The corresponding memristor exhibits analog-type resistive-variable characteristics with an ON/OFF ratio of ∼1000, stable data retention after 10,000 s, and no noticeable fluctuation in resistance after 10,000 pulse cycles. Electron energy loss spectroscopy, first-principles calculations, and electrical measurements reveal that compensating or restoring oxygen vacancies at the NSTO surface decreases or increases the contact barrier between PV-SFO and NSTO, respectively, thereby gradually regulating the resistance value. Furthermore, high-quality epitaxial PV-SFO/NSTO devices achieve up to 98.21% recognition accuracy for handwriting recognition tasks using LeNet-5-based network structures and 92.21% accuracy for color images using visual geometry group (VGG) network structures. This work contributes to the advancement of interface-type memristors and provides valuable insights into enhancing synaptic functionality in neuromorphic computing systems.

Informatics-Based Learning of Oxygen Vacancy Ordering Principles in Oxygen-Deficient Perovskites

Ordered oxygen vacancies (OOVs) in perovskites can exhibit long-range order and may be used to direct materials properties through modifications in electronic structures and broken symmetries. Based on the various vacancy patterns observed in previously known compounds, we explore the ordering principles of oxygen-deficient perovskite oxides with ABO2.5 stoichiometry to identify other OOV variants. We performed first-principles calculations to assess the OOV stability on a data set of 50 OOV structures generated from our bespoke algorithm. The algorithm employs uniform planar vacancy patterns on (111) pseudocubic perovskite layers and the approach proves effective for generating stable OOV patterns with minimal computational loads. We find as expected that the major factors determining the stability of OOV structures include coordination preferences of transition metals and elastic penalties resulting from the assemblies of polyhedra. Cooperative rotational modes of polyhedra within the OOV structures reduce elastic instabilities by optimizing the bond valence of A- and B cations. This finding explains the observed formation of vacancy channels along low-index crystallographic directions in prototypical OOV phases. The identified ordering principles enable us to devise other stable vacancy patterns with longer periodicity for targeted property design in yet to be synthesized compounds.

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.

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.

Guided anisotropic oxygen transport in vacancy ordered oxides

Anisotropic and efficient transport of ions under external stimuli governs the operation and failure mechanisms of energy-conversion systems and microelectronics devices. However, fundamental understanding of ion hopping processes is impeded by the lack of atomically precise materials and probes that allow for the monitoring and control at the appropriate time- and length- scales. In this work, using in-situ transmission electron microscopy, we directly show that oxygen ion migration in vacancy ordered, semiconducting SrFeO2.5 epitaxial thin films can be guided to proceed through two distinctly different diffusion pathways, each resulting in different polymorphs of SrFeO2.75 with different ground electronic properties before reaching a fully oxidized, metallic SrFeO3 phase. The diffusion steps and reaction intermediates are revealed by means of ab-initio calculations. The principles of controlling oxygen diffusion pathways and reaction intermediates demonstrated here may advance the rational design of structurally ordered oxides for tailored applications and provide insights for developing devices with multiple states of regulation.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory

A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7  A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).

Aliovalent Ta-Doping-Engineered Oxygen Vacancy Configurations for Ultralow-Voltage Resistive Memory Devices: A DFT-Supported Experimental Study

Alteration of transport properties of any material, especially metal oxides, by doping suitable impurities is not straightforward as it may introduce multiple defects like oxygen vacancies (V_o) in the system. It plays a decisive role in controlling the resistive switching (RS) performance of metal oxide-based memory devices. Therefore, a judicious choice of dopants and their atomic concentrations is crucial for achieving an optimum V_o configuration. Here, we show that the rational designing of RS memory devices with cationic dopants (Ta), in particular, Au/Ti_1-xTa_xO_2-δ/Pt devices, is promising for the upcoming non-volatile memory technology. Indeed, a current window of ∼10⁴ is realized at an ultralow voltage as low as 0.25 V with significant retention (∼10⁴ s) and endurance (∼10⁵ cycles) of the device by considering 1.11 at % Ta doping. The obtained device parameters are compared with those in the available literature to establish its excellent performance. Furthermore, using detailed experimental analyses and density functional theory (DFT)-based first-principles calculations, we comprehend that the meticulous presence of V_o configurations and the columnar-like dendritic structures is crucial for achieving ultralow-voltage bipolar RS characteristics. In fact, the dopant-mediated V_o interactions are found to be responsible for the enhancement in local current conduction, as evidenced from the DFT-simulated electron localization function plots, and these, in turn, augment the device performance. Overall, the present study on cationic-dopant-controlled defect engineering could pave a neoteric direction for future energy-efficient oxide memristors.

Dynamics of Anisotropic Oxygen-Ion Migration in Strained Cobaltites

Orientation control of the oxygen vacancy channel (OVC) is highly desirable for tailoring oxygen diffusion as it serves as a fast transport channel in ion conductors, which is widely exploited in solid-state fuel cells, catalysts, and ion-batteries. Direct observation of oxygen-ion hopping toward preferential vacant sites is a key to clarifying migration pathways. Here we report anisotropic oxygen-ion migration mediated by strain in ultrathin cobaltites via in situ thermal activation in atomic-resolved transmission electron microscopy. Oxygen migration pathways are constructed on the basis of the atomic structure during the OVC switching, which is manifested as the vertical-to-horizontal OVC switching under tensile strain but the horizontal-to-diagonal switching under compression. We evaluate the topotactic structural changes to the OVC, determine the crucial role of the tolerance factor for OVC stability, and establish the strain-dependent phase diagram. Our work provides a practical guide for engineering OVC orientation that is applicable to ionic-oxide electronics.

ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO₃ multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO₃ multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO₃-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO₃-based memristive application in artificial intelligence.

Electronic-structure evolution of SrFeO3-xduring topotactic phase transformation

Oxygen-vacancy-induced topotactic phase transformation between the ABO_2.5brownmillerite structure and the ABO₃perovskite structure attracts ever-increasing attention due to the perspective applications in catalysis, clean energy field, and memristors. However, a detailed investigation of the electronic-structure evolution during the topotactic phase transformation for understanding the underlying mechanism is highly desired. In this work, multiple analytical methods were used to explore evolution of the electronic structure of SrFeO_3-xthin films during the topotactic phase transformation. The results indicate that the increase in oxygen content induces a new unoccupied state of O 2pcharacter near the Fermi energy, inducing the insulator-to-metal transition. More importantly, the hole states are more likely constrained to thedx²-y²orbital than to thed3z²-r²orbital. Our results reveal an unambiguous evolution of the electronic structure of SrFeO_3-xfilms during topotactic phase transformation, which is crucial not only for fundamental understanding but also for perspective applications such as solid-state oxide fuel cells, catalysts, and memristor devices.

Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning

Inspired by the human brain, nonvolatile memories (NVMs)-based neuromorphic computing emerges as a promising paradigm to build power-efficient computing hardware for artificial intelligence. However, existing NVMs still suffer from physically imperfect device characteristics. In this work, a topotactic phase transition random-access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoO _x is demonstrated. The reversible phase transition of SrCoO _x is well controlled by oxygen ion migrations along the highly ordered oxygen vacancy channels, enabling reproducible analog switching characteristics with reduced variability. Combining density functional theory and kinetic Monte Carlo simulations, the orientation-dependent switching mechanism of TPT-RAM is investigated synergistically. Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible memristive synapses for neuromorphic computing.

Research on Pt/NiOx/WO3-x:Ti/W Multijunction Memristors with Synaptic Learning and Memory Functions

Artificial synapses based on biological synapses represent a new idea in the field of artificial intelligence with future applications. Current two-terminal RRAM devices have developed tremendously due to the adjustable synaptic plasticity of artificial synapses. However, these devices still have some problems, such as current leakage and poor durability. Here, we demonstrate a Pt/NiO_x/WO_3-x:Ti/W memristor with a pn-type heterojunction and two metal-semiconductor contacts, which exhibits good rectification. Due to the change in the internal potential barrier, the devices possess multiconductance states under different pulse modulations and memory characteristics, similar to synapses. The rectification characteristics of the device exhibit stable enhancement and suppression behavior. Each device in the 10 × 10 cross array we constructed can be written correctly, which verifies that leakage current does not appear in the device. The structure proposed in this work has great significance for the integration of large-scale memristor cross arrays.