Multilevel Ultrafast Flexible Nanoscale Nonvolatile Hybrid Graphene Oxide-Titanium Oxide Memories

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Graphene-based RRAM devices for neural computing

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

Graphene/MoS2-xOx/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing

Conventional artificial intelligence (AI) machine vision technology, based on the von Neumann architecture, uses separate sensing, computing, and storage units to process huge amounts of vision data generated in sensory terminals. The frequent movement of redundant data between sensors, processors and memory, however, results in high-power consumption and latency. A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal simultaneously. Here, we proposed a non-volatile photomemristor, in which the reconfigurable responsivity can be modulated by the charge and/or photon flux through it and further stored in the device. The non-volatile photomemristor has a simple two-terminal architecture, in which photoexcited carriers and oxygen-related ions are coupled, leading to a displaced and pinched hysteresis in the current-voltage characteristics. For the first time, non-volatile photomemristors implement computationally complete logic with photoresponse-stateful operations, for which the same photomemristor serves as both a logic gate and memory, using photoresponse as a physical state variable instead of light, voltage and memresistance. The polarity reversal of photomemristors shows great potential for in-memory sensing and computing with feature extraction and image recognition for neuromorphic vision.

Ultralow-Power RRAM with a High Switching Ratio Based on the Large van der Waals Interstice Radius of TMDs

Low power and high switching ratio are the development direction of the next generation of resistive random access memory (RRAM). Previous techniques could not increase the switching ratio while reducing the SET power. Here, we report a method to fabricate low-power and high-switching-ratio RRAM by adjusting the interstice radius (r_g) between the van der Waals (vdW) layers of transitional-metal dichalcogenides (TMDs), which simultaneously increases the switching ratio and reduces the SET power. The SET voltage, SET power, switching ratio and endurance of the device are strongly correlated with r_g. When the ratio of r_g to the radius of the metal ions that form the conductive filaments (r_g/r_Ag⁺) is near 1, the SET voltage and SET power vertically decrease while the switching ratio vertically rises with increasing r_g/r_Ag⁺. For the fabricated Ag/[SnS₂/poly(methyl methacrylate)]/Cu RRAM with an r_g/r_Ag⁺ of 1.04, the SET voltage, SET power and switching ratio are 0.14 V, 10^-10 W and 10⁶, respectively. After 10⁴ switching cycles and a 10⁴ s retention time, the switching ratio of the device can still be stable above 10⁶. Bending has no influence on the performance of the device when the bending radius is not <2 mm.

Flexible Memristor Devices Using Hybrid Polymer/Electrodeposited GeSbTe Nanoscale Thin Films

We report on the development of hybrid organic-inorganic material-based flexible memristor devices made by a fast and simple electrochemical fabrication method. The devices consist of a bilayer of poly(methyl methacrylate) (PMMA) and Te-rich GeSbTe chalcogenide nanoscale thin films sandwiched between Ag top and TiN bottom electrodes on both Si and flexible polyimide substrates. These hybrid memristors require no electroforming process and exhibit reliable and reproducible bipolar resistive switching at low switching voltages under both flat and bending conditions. Multistate switching behavior can also be achieved by controlling the compliance current (CC). We attribute the switching between the high resistance state (HRS) and low resistance state (LRS) in the devices to the formation and rupture of conductive Ag filaments within the hybrid PMMA/GeSbTe matrix. This work provides a promising route to fabricate flexible memory devices through an electrodeposition process for application in flexible electronics.

Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiOx-Based Exponential Selector

Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiO_x/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfO_x/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O^2- and Ti³⁺ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.

Low energy consumption fiber-type memristor array with integrated sensing-memory

The increasing growth of electronic information science and technology has triggered the renaissance of the artificial sensory nervous system (SNS), which can emulate the response of organisms towards external stimuli with high efficiency. In traditional SNS, the sensor units and the memory units are separated, and therefore difficult to miniaturize and integrate. Here, we have incorporated the sensor unit and the memory unit into one system, taking advantage of the unique properties of the ion-gel system. Meanwhile, the weaving-type memory array presents paramount advantages of integration and miniaturization and conformal lamination to curved surfaces. It is worth noting that the electrical double layer (EDL) within the ion gel endow the device with a low operation voltage (<1 V) to achieve low energy consumption. Finally, according to the relationship of pressure stimuli and electrical behavior, the integrated responsiveness-storage external stimuli ability is achieved. Our work offers a new platform for designing cutting edge SNS.

Research Progress of Biomimetic Memristor Flexible Synapse

With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.

Flexible Memory Device Composed of Metal-Oxide and Two-Dimensional Material (SnO2/WTe2) Exhibiting Stable Resistive Switching

Two-terminal, non-volatile memory devices are the fundamental building blocks of memory-storage devices to store the required information, but their lack of flexibility limits their potential for biological applications. After the discovery of two-dimensional (2D) materials, flexible memory devices are easy to build, because of their flexible nature. Here, we report on our flexible resistive-switching devices, composed of a bilayer tin-oxide/tungsten-ditelluride (SnO2/WTe2) heterostructure sandwiched between Ag (top) and Au (bottom) metal electrodes over a flexible PET substrate. The Ag/SnO2/WTe2/Au flexible devices exhibited highly stable resistive switching along with an excellent retention time. Triggering the device from a high-resistance state (HRS) to a low-resistance state (LRS) is attributed to Ag filament formation because of its diffusion. The conductive filament begins its development from the anode to the cathode, contrary to the formal electrochemical metallization theory. The bilayer structure of SnO2/WTe2 improved the endurance of the devices and reduced the switching voltage by up to 0.2 V compared to the single SnO2 stacked devices. These flexible and low-power-consumption features may lead to the construction of a wearable memory device for data-storage purposes.

Tunable biological nonvolatile multilevel data storage devices

The speed with which electronic products are updated is continuously increasing. Consequently, since waste electronic products can cause serious environmental pollution, the demand for electronic products made of biological materials is becoming increasingly urgent. Although biological memristors have significant advantages, their electrical characteristics still do not meet the requirements to be used in future nonvolatile memories. Therefore, how to control their electrical characteristics has become a popular topic of research. In this study, tunable biomemristors with an Al/tussah blood (TB)-carbon nanotube (CNT)/indium tin oxide (ITO)/glass structure were fabricated. Such a device exhibits stable bipolar resistance switching behavior and good retention characteristics (10⁴ s). Experimental results show that the ON/OFF current ratio can be effectively controlled by modifying the CNT concentration in the TB-CNT composite film. Multilevel (8 levels, 3 bits per cell) storage capabilities can be achieved in the device by controlling its compliance current in order to achieve high-density storage. The resistance switching behavior originates from the formation and rupture of conductive oxygen vacancy filaments. TB is a promising natural biomaterial in the field of green electronics, and this research could blaze a new trail for the development of biological memory devices. Biomemristors with multilevel resistance states can be used as electronic synapses and are one of the choices for simulating biological synapses.

Ternary Conductance Switching Realized by a Pillar[5]arene-Functionalized Two-Dimensional Imine Polymer Film

Nowadays, most manufacturing memory devices are based on materials with electrical bistability (i. e., "0" and "1") in response to an applied electric field. Memory devices with multilevel states are highly desired so as to produce high-density and efficient memory devices. Herein, we report the first multichannel strategy to realize a ternary-state memristor. We make use of the intrinsic sub-nanometer channel of pillar[5]arene and nanometer channel of a two-dimensional imine polymer to construct an active layer with multilevel channels for ternary memory devices. Low threshold voltage, long retention time, clearly distinguishable resistance states, high ON/OFF ratio (OFF/ON1/ON2=1 : 10 : 10³ ), and high ternary yield (75 %) were obtained. In addition, the flexible memory device based on 2DP_TPAZ+TAPB can maintain its stable ternary memory performance after being bent 500 times. The device also exhibits excellent thermal stability and can tolerate a temperature as high as 300 °C. It is envisioned that the results of this work will open up possibilities for multistate, flexible resistive memories with good thermal stability and low energy consumption, and broaden the application of pillar[n]arene.

Bioresistive Random-Access Memory with Gold Nanoparticles that Generate the Coulomb Blocking Effect Can Realize Multilevel Data Storage and Synapse Simulation

Gold nanoparticles (Au NPs) have good biocompatibility and special quantum effects. In this Letter, we embedded Au NPs into silkworm hemolymph (SH) to improve the performance of the device and fabricated Al/SH:Au NPs/indium tin oxide (ITO)/glass resistive random access memory. The device exhibits a bipolar switching behavior with a retention time of 10⁴ s. Compared with the Al/SH/ITO device without Au NPs, the device has a higher ON/OFF current ratio (>10⁵) and a smaller V_reset. The improvement in device performance is attributed to the fact that Au NPs act as the electron-trapping center in the device; a Coulomb blockade occurs after electrons are trapped, thereby increasing the resistance of the device in the high-resistance state. Using optimized devices can realize multilevel data storage and can also simulate synaptic characteristics such as potentiation and depression. The device is expected to be applied to high-density, low-cost, degradable, and biocompatible storage devices and neuromorphic computing in the future.

Functional bipolar resistive switching in AlN/Ni-Mn-In based magnetoelectric heterostructure

This report explores the influence of temperature on resistive switching characteristics in the AlN/Ni-Mn-In magnetoelectric (ME) heterostructure-based resistive random access memory (ReRAM) device. The fabricated Cu/AlN/Ni-Mn-In/Si device exhibits a sharp transition from a high resistance state (HRS) to low resistance state (LRS) at a SET voltage. The rupture of the filament from its weakest point at a RESET voltage turn the device back to its HRS. The stable bipolar resistive switching behavior is described by the current-voltage (I-V) characteristic. The HRS and LRS are explained by the trap-controlled space charge limited conduction mechanism and a well-known Ohmic conduction mechanism, respectively. The temperature-dependent resistance has been observed to further confirm the conduction mechanism in HRS and LRS. The current conduction in LRS is explained by an analytical model based on copper metallic filament formation via Cu⁺migration from the top to the bottom electrode. A significant change in the SET voltage has been observed with the decrease in temperature. This variation in the SET voltage is explained via strain-mediated coupling in interfacially connected AlN/Ni-Mn-In ME heterostructure. The fabricated device displays an appreciable OFF/ON ratio of the order ∼3 × 10³with good endurance and retention of ∼1000 cycles and ∼900 s, respectively. A slight variation (<40%) in SET and RESET voltages has been observed for total endurance cycles. This study demonstrates the importance of ME heterostructure for futuristic tuneable ReRAM applications.

Decade of 2D-materials-based RRAM devices: a review

Two dimensional (2D) materials have offered unique electrical, chemical, mechanical and physical properties over the past decade owing to their ultrathin, flexible, and multilayer structure. These layered materials are being used in numerous electronic devices for various applications, and this review will specifically focus on the resistive random access memories (RRAMs) based on 2D materials and their nanocomposites. This study presents the device structures, conduction mechanisms, resistive switching properties, fabrication technologies, challenges and future aspects of 2D-materials-based RRAMs. Graphene, derivatives of graphene and MoS2 have been the major contributors among 2D materials for the application of RRAMs; however, other members of this family such as hBN, MoSe2, WS2 and WSe2 have also been inspected more recently as the functional materials of nonvolatile RRAM devices. Conduction in these devices is usually dominated by either the penetration of metallic ions or migration of intrinsic species. Most prominent advantages offered by RRAM devices based on 2D materials include fast switching speed (<10 ns), less power losses (10 pJ), lower threshold voltage (<1 V) long retention time (>10 years), high electrical endurance (>108 voltage cycles) and extended mechanical robustness (500 bending cycles). Resistive switching properties of 2D materials have been further enhanced by blending them with metallic nanoparticles, organic polymers and inorganic semiconductors in various forms.

Non-Polar and Complementary Resistive Switching Characteristics in Graphene Oxide devices with Gold Nanoparticles: Diverse Approach for Device Fabrication

Downscaling limitations and limited write/erase cycles in conventional charge-storage based non-volatile memories stimulate the development of emerging memory devices having enhanced performance. Resistive random-access memory (RRAM) devices are recognized as the next-generation memory devices for employment in artificial intelligence and neuromorphic computing, due to their smallest cell size, high write/erase speed and endurance. Unipolar and bipolar resistive switching characteristics in graphene oxide (GO) have been extensively studied in recent years, whereas the study of non-polar and complementary switching is scarce. Here we fabricated GO-based RRAM devices with gold nanoparticles (Au Nps). Diverse types of switching behavior are observed by changing the processing methods and device geometry. Tri-layer GO-based devices illustrated non-polar resistive switching, which is a combination of unipolar and bipolar switching. Five-layer GO-based devices depicted complementary resistive switching having the lowest current values ~12 µA; and this structure is capable of resolving the sneak path issue. Both devices show good retention and endurance performance. Au Nps in tri-layer devices assisted the conducting path, whereas in five-layer devices, Au Nps layer worked as common electrodes between co-joined cells. These GO-based devices with Au Nps comprising different configuration are vital for practical applications of emerging non-volatile resistive memories.

Controlled inter-state switching between quantized conductance states in resistive devices for multilevel memory

A detailed understanding of quantization conductance (QC), the correlation with resistive switching phenomena and controlled manipulation of quantized states is crucial for realizing atomic-scale multilevel memory elements. Here, we demonstrate highly stable and reproducible quantized conductance states (QC-states) in Al/niobium oxide/Pt resistive switching devices. Three levels of control over the QC-states, required for multilevel quantized state memories, like, switching ON to different quantized states, switching OFF from quantized states, and controlled inter-state switching among one QC state to another has been demonstrated by imposing limiting conditions of stop-voltage and current compliance. The well-defined multiple QC states along with a working principle for switching among various states show promise for implementation of multilevel memory devices.

Role of GO and r-GO in resistance switching behavior of bilayer TiO2 based RRAM

Graphene-based resistance random access memory devices (RRAMs) have shown promise as a suitable replacement for flash memories, owing to their fast switching speed, low programming voltage, better scalability and great reliability. Furthermore, recent research works have shown bi-layer RRAM devices exhibiting better performance along the same parameters, where titania is one of the most commonly used materials. In the present work, we have studied the resistance switching behavior in a bi-layer RRAM device structure of TiO₂ with graphene oxide (GO) and reduced graphene oxide (rGO). Switching mechanism in these devices has been investigated by detailed experimental characterization in conjunction with a finite element modeling (FEM) simulation. A dual conical conductive filament has been used in the present work, based on the modeling of the electroforming process carried out by FEM. It has been demonstrated that for the GO/TiO₂ based hybrid RRAM device structure, GO acts as an active filament formation layer, whereas in the rGO/TiO₂ bi-layer structure, rGO acts as a mere electrode.

Piezophototronic Effect Modulated Multilevel Current Amplification from Highly Transparent and Flexible Device Based on Zinc Oxide Thin Film

In this work, a strain modulated highly transparent and flexible ZnO/Ag-nanowires/polyethylene terephthalate optoelectronic device is developed. By utilizing the applied external strain-induced piezophototronic effects of a ZnO thin film, a UV-generated photocurrent is tuned in a wide range starting from 0.01 to 85.07 µA and it is presented in a comprehensive map. Particularly, the performance of the device is effectively enhanced 7733 times by compressive strain, as compared to its dark current in a strain-free state. The observed results are explained quantitatively based on the modulation of oxygen desorption/absorption on the ZnO surface under the influence of applied strains. The presented simple optoelectronic device can be easily integrated into existing planar structures, with potential applications in highly transparent smart windows, wearable electronics, smartphones, security communication, and so on.

Graphite-based selectorless RRAM: improvable intrinsic nonlinearity for array applications

Selectorless graphite-based resistive random-access memory (RRAM) has been demonstrated by utilizing the intrinsic nonlinear resistive switching (RS) characteristics, without an additional selector or transistor for low-power RRAM array application. The low effective dielectric constant value (k) layer of graphite or graphite oxide is utilized, which is beneficial in suppressing sneak-path currents in the crossbar RRAM array. The tail-bits with low nonlinearity can be manipulated by the positive voltage pulse, which in turn can alleviate variability and reliability issues. Our results provide additional insights for built-in nonlinearity in 1R-only selectorless RRAMs, which are applicable to the low-power memory array, ultrahigh density storage, and in-memory neuromorphic computational configurations.

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification

For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy-efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device-to-system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.

In Situ Observation of Resistive Switching in an Asymmetric Graphene Oxide Bilayer Structure

Graphene oxide decorated with oxygen functional groups is a promising candidate as an active layer in resistive switching devices due to its controllable physical-chemical properties, high flexibility, and transparency. However, the origin of conductive channels and their growth dynamics remain a major challenge. We use in situ transmission electron microscopy techniques to demonstrate that nanoscale graphene oxide sheets bonded with oxygen dynamically change their physical and chemical structures upon an applied electric field. Artificially engineered bilayer reduced graphene oxide films with asymmetric oxygen content exhibit nonvolatile write-once-read-many memory behaviors without experiencing the bubble destruction due to the efficient migration of oxygen ions. We clearly observe that a conductive graphitic channel with a conical shape evolves from the upper oxygen-rich region to the lower oxygen-poor region. These findings provide fundamental guidance for understanding the oxygen motions of oxygen-containing carbon materials for future carbon-based nanoelectronics.

Effects of electric current on individual graphene oxide sheets combining in situ transmission electron microscopy and Raman spectroscopy

Graphene oxide (GO) is currently the object of extensive research because of its potential use in mass production of graphene-based materials, but also due to its tunability which holds great promise for new nanoscale electronic devices and sensors. To obtain a better understanding of the role of GO in electronic nano-devices, the elucidation of the effects of electrical current on a single GO sheet is of great interest. In this work, in situ transmission electron microscopy is used to study the effects of the electrical current flow through single GO sheets using an scanning tunneling microscope holder. In order to correlate the applied current with the structural properties of GO, Raman spectroscopy is carried out and data analysis is used to obtain information regarding the reduction grade and the disorder degree of the GO sheets before and after the application of current.

Compliance-Free Multileveled Resistive Switching in a Transparent 2D Perovskite for Neuromorphic Computing

We demonstrate the pulsed voltage tunable multileveled resistive switching (RS) across a promising transparent energy material of (C₄H₉NH₃)₂PbBr₄. The X-ray diffraction and scanning electron microscopy results confirm the growth of (001) plane-orientated nanostructures of (C₄H₉NH₃)₂PbBr₄ with an average size of ∼360 nm. The device depicts optical transmittance higher than 70% in the visible region and efficient absorbance in the ultraviolet region. The current-voltage measurement shows the bipolar RS. In addition, depending on the magnitude of applied electric pulse, the current across the device can be flipped in four different levels, which remain stable for long time, indicating multimode RS. Further, the current across the device increases gradually by applying continuous pulses, similar to the biological synapses. The observed results are attributed to the electric field-induced ionic migration across the (C₄H₉NH₃)₂PbBr₄. The existing study should open a new avenue to apply this promising energy material of perovskite for multifunctional advanced devices.

Highly Flexible Resistive Switching Memory Based on the Electronic Switching Mechanism in the Al/TiO2/Al/Polyimide Structure

A highly flexible resistive switching (RS) memory was fabricated in the Al/TiO₂/Al/polyimide structure using a simple and cost-effective method. An electronic-resistive-switching-based flexible memory with high performance that can withstand a bending strain of up to 3.6% was obtained. The RS properties showed no obvious degradation even after the bending tests that were conducted up to 10 000 times, and over 4000 writing/erasing cycles were confirmed at the maximally bent state. The superior electrical properties against the mechanical stress of the device can be ascribed to the electronic RS mechanism related to electron trapping/detrapping, which can prevent the inevitable degradation in the case of the RS related with the ionic defects.

Reliable Multistate Data Storage with Low Power Consumption by Selective Oxidation of Pyramid-Structured Resistive Memory

Multilevel data storage using resistive random access memory (RRAM) has attracted significant attention for addressing the challenges associated with the rapid advances in information technologies. However, it is still difficult to secure reliable multilevel resistive switching of RRAM due to the stochastic and multiple formation of conductive filaments (CFs). Herein, we demonstrate that a single CF, derived from selective oxidation by a structured Cu active electrode, can solve the reliability issue. High-quality pyramidal Cu electrodes with a sharp tip are prepared via the template-stripping method. Morphology-dependent surface energy facilitates the oxidation of Cu atoms at the tip rather than in other regions, and the tip-enhanced electric fields can accelerate the transport of the generated Cu ions. As a result, CF growth occurs mainly at the tip of the pyramidal electrode, which is confirmed by high-resolution electron microscopy and elemental analysis. The RRAM exhibits highly uniform and low forming voltages (the average forming voltage and its standard deviation for 20 pyramid-based RRAMs are 0.645 and 0.072 V, respectively). Moreover, all multilevel resistance states for the RRAMs are clearly distinguished and show narrow distributions within 1 order of magnitude, leading to reliable cell-to-cell performance for MLC operation.