Design of defect-chemical properties and device performance in memristive systems

Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.

Identification of Chemical and Structural Characteristics of Acrylic Paint Layer Using Terahertz Metasurfaces

The precise investigation and monitoring of the internal structural change within complex layered systems are crucial, as the emergence of undesirable defects or formation of secondary internal structures significantly exerts a profound influence on the overall properties of the system. We demonstrate an advanced sensing platform utilizing terahertz metasurfaces, allowing chemical detection and precise identification within an acrylic paint layer with a noticeable sensitivity, reaching down to several hundreds of nanometers, in nondestructive and noncontact manners. The identification of solid and mixed paint samples was achieved by analyzing their optical properties, including the refractive index and absorption coefficient. Notably, the presence of internal pore defects within the mixed acrylic paint led to geometric distortions, affecting the state of the overall system. Intriguingly, even in cases where acrylic paint exhibited identical colors perceptible under visible light, distinct discrimination and identification of chemical compositions were successfully proposed.

Gamma radiation-induced nanodefects in diffusive memristors and artificial neurons

Gamma photons with an average energy of 1.25 MeV are well-known to generate large amounts of defects in semiconductor electronic devices. Here we investigate the novel effect of gamma radiation on diffusive memristors based on metallic silver nanoparticles dispersed in a dielectric matrix of silica. Our experimental findings show that after exposure to radiation, the memristors and artificial neurons made of them demonstrate much better performance in terms of stable volatile resistive switching and higher spiking frequencies, respectively, compared to the pristine samples. At the same time we observe partial oxidation of silver and reduction of silicon within the switching silica layer. We propose nanoinclusions of reduced silicon distributed across the silica layer to be the backbone for metallic nanoparticles to form conductive filaments, as supported by our theoretical simulations of radiation-induced changes in the diffusion process. Our findings propose a new opportunity to engineer the required characteristics of diffusive memristors in order to emulate biological neurons and develop bio-inspired computational technology.

A High-Entropy-Oxides-Based Memristor: Outstanding Resistive Switching Performance and Mechanisms in Atomic Structural Evolution

The application of high-entropy oxide (HEO) has attracted significant attention in recent years owing to their unique structural characteristics, such as excellent electrochemical properties and long-term cycling stability. However, the application of resistive random-access memory (RRAM) has not been extensively studied, and the switching mechanism of HEO-based RRAM has yet to be thoroughly investigated. In this study, HEO (Cr, Mn, Fe, Co, Ni)3 O4 with a spinel structure is epitaxially grown on a Nb:STO conductive substrate, and Pt metal is deposited as the top electrode. After the resistive-switching operation, some regions of the spinel structure are transformed into a rock-salt structure and analyzed using advanced transmission electron microscopy and scanning transmission electron microscopy. From the results of X-ray photoelectron spectroscopy and electron energy loss spectroscopy, only specific elements would change their valence state, which results in excellent resistive-switching properties with a high on/off ratio on the order of 105 , outstanding endurance (>4550 cycles), long retention time (>104 s), and high stability, which suggests that HEO is a promising RRAM material.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

Impurity-driven simultaneous size and crystallinity control of metal nanoparticles

Both the size and crystallinity should be optimized for practical applications utilizing metallic nanoparticles because they strongly influence the nanoparticles property. Herein a liquid phase chemical reduction method controls the defects (crystallinity) in metallic Cu nanoparticles simply and easily. Although the addition of an impurity substance, which cannot be thermodynamically alloyed with Cu, reduces the crystallinity of synthesized Cu nanoparticles, it also affects the deposition behavior, and consequently, the nanoparticle size changes unexpectedly. Therefore, a precise control of the synthesis condition is required to synthesize the nanoparticles having optimal size and crystallinity. To clarify the nanoparticle formation mechanism in an impurity-containing solution, the catalytic activity of the reductant and the redox potential change due to the metastable product are electrochemically evaluated to reveal the correlation between nanoparticle formation behavior and synthesis condition. Finally, the synthesis of two types of Cu nanoparticles, which have similar sizes but different crystallinities is demonstrated. This simple nanomaterial design approach to control the crystallinity and the interpretation of the deposition process in an impurity-containing condition should be widely applicable to metallic nanoparticle syntheses.

A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses

Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.

Self-aligned patterning of tantalum oxide on Cu/SiO2 through redox-coupled inherently selective atomic layer deposition

Atomic-scale precision alignment is a bottleneck in the fabrication of next-generation nanoelectronics. In this study, a redox-coupled inherently selective atomic layer deposition (ALD) is introduced to tackle this challenge. The 'reduction-adsorption-oxidation' ALD cycles are designed by adding an in-situ reduction step, effectively inhibiting nucleation on copper. As a result, tantalum oxide exhibits selective deposition on various oxides, with no observable growth on Cu. Furthermore, the self-aligned TaO_x is successfully deposited on Cu/SiO₂ nanopatterns, avoiding excessive mushroom growth at the edges or the emergence of undesired nucleation defects within the Cu region. The film thickness on SiO₂ exceeds 5 nm with a selectivity of 100%, marking it as one of the highest reported to date. This method offers a streamlined and highly precise self-aligned manufacturing technique, which is advantageous for the future downscaling of integrated circuits.

Environmentally Stable and Reconfigurable Ultralow-Power Two-Dimensional Tellurene Synaptic Transistor for Neuromorphic Edge Computing

While neuromorphic computing can define a new era for next-generation computing architecture, the introduction of an efficient synaptic transistor for neuromorphic edge computing still remains a challenge. Here, we envision an atomically thin 2D Te synaptic device capable of achieving a desirable neuromorphic edge computing design. The hydrothermally grown 2D Te nanosheet synaptic transistor apparently mimicked the biological synaptic nature, exhibiting 100 effective multilevel states, a low power consumption of ∼110 fJ, excellent linearity, and short-/long-term plasticity. Furthermore, the 2D Te synaptic device achieved reconfigurable MNIST recognition accuracy characteristics of 88.2%, even after harmful detergent environment infection. We believe that this work serves as a guide for developing futuristic neuromorphic edge computing.

Highly anisotropic and ultra-diffusive vacancies in α-antimonene

α-Antimonene has recently been successfully fabricated in experiment; hence, it is timely to examine how various types of point defects in α-antimonene can affect its novel electronic properties. Herein, we present a comprehensive investigation of a total of nine possible types of point defects in α-antimonene via first-principles calculations. Particular attention is placed on the structural stability of the point defects and the effects of point defects on the electronic properties of α-antimonene. Compared with its structural analogs, such as phosphorene, graphene, and silicene, we find that most defects in α-antimonene can be more easily generated, and that among the nine types of point defects, the single vacancy SV-(5|9) is likely the most stable one while its presence can be orders of magnitude higher in concentration than that in phosphorene. Moreover, we find that the vacancy exhibits anisotropic and low diffusion barriers, of merely 0.10/0.30 eV in the zigzag/armchair direction. Notably, at room temperature, the migration of SV-(5|9) in the zigzag direction of α-antimonene is estimated to be three orders faster than that along the armchair direction, and also three orders faster than that of phosphorene in the same direction. Overall, the point defects in α-antimonene can significantly affect the electronic properties of the host two-dimensional (2D) semiconductor and thus the light absorption capability. The anisotropic, ultra-diffusive, and charge tunable single vacancies, along with the high oxidation resistance, render the α-antimonene sheet a unique 2D semiconductor (beyond the phosphorene) for developing vacancy-enabled nanoelectronics.

Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices

Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Organismic Memristive Structures With Variable Functionality for Neuroelectronics

In this paper, we report an approach to design nanolayered memristive compositions based on TiO2/Al2O3 bilayer structures with analog non-volatile and volatile tuning of the resistance. The structure of the TiO2 layer drives the physical mechanism underlying the non-volatile resistance switching, which can be changed from electronic to ionic, enabling the synaptic behavior emulation. The presence of the anatase phase in the amorphous TiO2 layer induces the resistive switching mechanism due to electronic processes. In this case, the switching of the resistance within the range of seven orders of magnitude is experimentally observed. In the bilayer with amorphous titanium dioxide, the participation of ionic processes in the switching mechanism results in narrowing the tuning range down to 2–3 orders of magnitude and increasing the operating voltages. In this way, a combination of TiO2/Al2O3 bilayers with inert electrodes enables synaptic behavior emulation, while active electrodes induce the neuronal behavior caused by cation density variation in the active Al2O3 layer of the structure. We consider that the proposed approach could help to explore the memristive capabilities of nanolayered compositions in a more functional way, enabling implementation of artificial neural network algorithms at the material level and simplifying neuromorphic layouts, while maintaining all benefits of neuromorphic architectures.

Filling the gap between topological insulator nanomaterials and triboelectric nanogenerators

Reliable energy modules and higher-sensitivity, higher-density, lower-powered sensing systems are constantly required to develop wearable electronics and the Internet of Things technology. As an emerging technology, triboelectric nanogenerators have been potentially guiding the landscape of sustainable power units and energy-efficient sensors. However, the existing triboelectric series is primarily populated by polymers and rubbers, limiting triboelectric sensing plasticity to some extent owing to their stiff surface electronic structures. To enrich the current triboelectric group, we explore the triboelectric properties of the topological insulator nanofilm by Kelvin probe force microscopy and reveal its relatively positive electrification charging performance. Both the larger surface potential difference and the conductive surface states of the nanofilms synergistically improve the charge transfer behavior between the selected triboelectric media, endowing the topological insulator-based triboelectric nanogenerator with considerable output performance. Besides serving as a wearable power source, the ultra-compact device array demonstrates innovative system-level sensing capabilities, including precise monitoring of dynamic objects and real-time signal control at the human-machine interface. This work fills the blank between topological quantum matters and triboelectric nanogenerators and, more importantly, exploits the significant potential of topological insulator nanofilms for self-powered flexible/wearable electronics and scalable sensing technologies.

Resistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability

Ion-based memristive switching has attracted widespread attention from industries owing to its outstanding advantages in storage and neuromorphic computing. Major issues for achieving brain-inspired computation of highly functional memory in redox-based ion devices are relatively large variability in their operating parameters and limited cycling endurance. In some devices, volatile and nonvolatile operations often replace each other without changing operating conditions. To address these issues, it is important to observe directly what is happening in repeated operations. Herein, we use a planar device that enables direct capturing of microscopic behaviors in the nucleation and growth of metal whiskers under repeated switching to verify the microscopic origin of the large parameter variability. We report direct observations that reveal the physical origin for the large cycle-to-cycle and device-to-device variability in memristive switching, which was achieved using planar polymer atomic switches with a gap >1 μm. We find that the deposition location of metal atoms is closely related to the crystallinity of the ion transport layer (solid polymer electrolyte, SPE). The filament variability (shape, position, quantity, etc.) during different cycles and devices is indeed the main reason for the observed variability in the operating characteristics. The results shed unique light on the complexity of the operation of the ion device, that is, the evolution of the dielectric layer and metal filament must be considered.

Technology agnostic frequency characterization methodology for memristors

Over the past decade, memristors have been extensively studied for a number of applications, almost exclusively with DC characterization techniques. Studies of memristors in AC circuits are sparse, with only a few examples found in the literature, and characterization methods with an AC input are also sparingly used. However, publications concerning the usage of memristors in this working regime are currently on the rise. Here we propose a "technology agnostic" methodology for memristor testing in certain frequency bands. A measurement process is initially proposed, with specific instructions on sample preparation, followed by an equipment calibration and measurement protocol. This article is structured in a way which aims to facilitate the usage of any available measurement equipment and it can be applied on any type of memristive technology. The second half of this work is centered around the representation of data received from following this process. Bode plot and Nyquist plot representations are considered and the information received from them is evaluated. Finally, examples of expected behaviors are given, characterizing simulated scenarios which represent different internal device models and different switching behaviors, such as capacitive or inductive switching. This study aims at providing a cohesive way for memristor characterization, to be used as a good starting point for frequency applications, and for understanding physical processes inside the devices, by streamlining the measuring process and providing a frame in which data representation and comparison will be facilitated.

Memristor-based biomimetic compound eye for real-time collision detection

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level

Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III-V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing.

The electric double layer effect and its strong suppression at Li+ solid electrolyte/hydrogenated diamond interfaces

The electric double layer (EDL) effect at solid electrolyte/electrode interfaces has been a key topic in many energy and nanoelectronics applications (e.g., all-solid-state Li⁺ batteries and memristors). However, its characterization remains difficult in comparison with liquid electrolytes. Herein, we use a novel method to show that the EDL effect, and its suppression at solid electrolyte/electronic material interfaces, can be characterized on the basis of the electric conduction characteristics of hydrogenated diamond(H-diamond)-based EDL transistors (EDLTs). Whereas H-diamond-based EDLT with a Li-Si-Zr-O Li⁺ solid electrolyte showed EDL-induced hole density modulation over a range of up to three orders of magnitude, EDLT with a Li-La-Ti-O (LLTO) Li⁺ solid electrolyte showed negligible enhancement, which indicates strong suppression of the EDL effect. Such suppression is attributed to charge neutralization in the LLTO, which is due to variation in the valence state of the Ti ions present. The method described is useful for quantitatively evaluating the EDL effect in various solid electrolytes.

Tunable Synaptic Characteristics of a Ti/TiO2/Si Memory Device for Reservoir Computing

In this study, we fabricate and characterize a Ti/TiO₂/Si device with different dopant concentrations on a silicon surface for neuromorphic systems. We verify the device stack using transmission electron microscopy (TEM). The Ti/TiO₂/p⁺⁺Si device exhibits interface-type bipolar resistive switching with long-term memory. The potentiation and depression by the pulses of various amplitudes are demonstrated using gradual resistive switching. Moreover, pattern-recognition accuracy (>85%) is obtained in the neuromorphic system simulation when conductance is used as the weight in the network. Next, we investigate the short-term memory characteristics of the Ti/TiO₂/p⁺Si device. The dynamic range is well-controlled by the pulse amplitude, and the conductance decay depends on the interval between the pulses. Finally, we build a reservoir computing system using the short-term effect of the Ti/TiO₂/p⁺Si device, in which 4 bits (16 states) are differentiated by various pulse streams through the device that can be used for pattern recognition.

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior

Resistive switching (RS) devices are promising forms of non-volatile memory. However, one of the biggest challenges for RS memory applications is the device-to-device (D2D) variability, which is related to the intrinsic stochastic formation and configuration of oxygen vacancy (V_O) conductive filaments (CFs). In order to reduce the D2D variability, control over the formation and configuration of oxygen vacancies is paramount. In this study, we report on the Zr doping of TaO_x-based RS devices prepared by pulsed-laser deposition as an efficient means of reducing the V_Oformation energy and increasing the confinement of CFs, thus reducing D2D variability. Our findings were supported by XPS, spectroscopic ellipsometry and electronic transport analysis. Zr-doped films showed increased V_Oconcentration and more localized V_Os, due to the interaction with Zr. DC and pulse mode electrical characterization showed that the D2D variability was decreased by a factor of seven, the resistance window was doubled, and a more gradual and monotonic long-term potentiation/depression in pulse switching was achieved in forming-free Zr:TaO_xdevices, thus displaying promising performance for artificial synapse applications.

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.

Enhancing Reliability of Studies on Single Filament Memristive Switching via an Unconventional cAFM Approach

Memristive devices are highly promising for implementing neuromorphic functionalities in future electronic hardware, and direct insights into memristive phenomena on the nanoscale are of fundamental importance to reaching this. Conductive atomic force microscopy (cAFM) has proven to be an essential tool for probing memristive action locally on the nanoscale, but the significance of the acquired data frequently suffers from the nonlocality associated with the thermal drift of the tip in ambient conditions. Furthermore, comparative studies of different configurations of filamentary devices have proven to be difficult, because of an immanent variability of the filament properties between different devices. Herein, these problems are addressed by constraining the memristive action directly at the apex of the probe through functionalization of a cAFM tip with an archetypical memristive stack, which is comprised of Ag/Si3N4. The design of such functionalized cantilevers (entitled here as "memtips") allowed the capture of the long-term intrinsic current response, identifying temporal correlations between switching events, and observing emerging spiking dynamics directly at the nanoscale. Utilization of an identical memtip for measurements on different counter electrodes made it possible to directly compare the impact of different device configurations on the switching behavior of the same filament. Such an analytical approach in ambient conditions will pave the way towards a deeper understanding of filamentary switching phenomena on the nanoscale.

Room-Temperature Manipulation of Magnetization Angle, Achieved with an All-Solid-State Redox Device

An all-solid-state redox device, composed of magnetite (Fe₃O₄) thin film and Li⁺ conducting electrolyte thin film, was fabricated for the manipulation of a magnetization angle at room temperature (RT). This is a key technology for the creation of efficient spintronics devices, but has not yet been achieved at RT by other carrier doping methods. Variations in magnetization angle and magnetic stability were precisely tracked through the use of planar Hall measurements at RT. The magnetization angle was reversibly manipulated at 10° by maintaining magnetic stability. Meanwhile, the manipulatable angle reached 56°, although the manipulation became irreversible when the magnetic stability was reduced. This large manipulation of magnetic angle was achieved through tuning of the 3d electron number and modulation of the internal strain in the Fe₃O₄ due to the insertion of high-density Li⁺ (approximately 10²¹ cm^-3). This RT manipulation is applicable to highly integrated spintronics devices due to its simple structure and low electric power consumption.

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO_{2- X} switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

Tunable Si Dangling Bond Pathway Induced Forming-Free Hydrogenated Silicon Carbide Resistive Switching Memory Device

With the coming of the big data age, the resistive switching memory (RSM) of three-dimensional (3D) high density shows a significant application in information storage and processing due to its simple structure and size-scalable characteristic. However, an electrical initialization process makes the peripheral circuits of 3D integration too complicated to be realized. Here a new forming-free SiC_x:H-based device can be obtained by tuning the Si dangling bond conductive channel. It is discovered that the forming-free behavior can be ascribed to the Si dangling bonds in the as-deposited SiC_x:H films. By tuning the number of Si dangling bonds, the forming-free SiC_x:H RSM exhibits a tunable memory window. The fracture and connection of the Si dangling bond conduction pathway induces the switching from the high-resistance state (HRS) to the low-resistance state (LRS). Our discovery of forming-free SiC_x:H resistive switching memory with tunable pathway opens a way to the realization of 3D high-density memory.