Resistive Switching Performance Improvement via Modulating Nanoscale Conductive Filament, Involving the Application of Two-Dimensional Layered Materials

Quantum dot-based memristors for information processing and artificial intelligence applications

Traditional computing systems struggle to keep pace with the development of artificial intelligence, as well as the development of the economy and continuous innovation in science and technology. Therefore, there is an urgent need for a new generation of powerful yet low-power computing technologies to replace them. Quantum dots have been incorporated into memristors due to their unique electrical properties, and the development of quantum dot memristors is expected to solve the problems faced by traditional memristors, including cycle stability, high energy consumption, and conductivity uniformity. This article reviews the research progress of quantum dot memristors and their simulation applications in artificial synapses. It summarizes some of the current challenges faced in the development of quantum dot memristors and discusses the potential future applications of these memristors in the field of artificial intelligence.

Digital Image Correlation and Numerical Analysis of Mechanical Behavior in Photopolymer Resin Lattice Structures

Cellular structures are increasingly utilized in modern engineering due to their exceptional mechanical and physical properties. In this study, the deformation and failure mechanisms of two energy-efficient lattice structures-hexagonal honeycomb and re-entrant honeycomb-were investigated. These structures were manufactured using additive stereolithography with light-curable Durable Resin V2. The experimental testing of the topologies under two perpendicular loading directions employed the 3D Digital Image Correlation (DIC) system to capture strain fields and deformation patterns, providing insights into structural behavior and failure mechanisms. The unit cells of the topologies were scaled up to enable precise optical measurements while preserving their structural interaction characteristics. Numerical simulations, conducted using the SAMP-1 material model in LS-DYNA and calibrated with tensile and compression test data, accurately replicated the behavior of the studied topologies and demonstrated good agreement with experimental results. The hexagonal structure, loaded along axis 2, showed the best fit, with deviations within 5%, while the re-entrant honeycomb structure exhibited weaker yet reasonable agreement. By integrating experimental and numerical approaches, the research validates the SAMP-1 model's predictive capabilities for lattice structures and provides a framework for analyzing energy-absorbing lattice topologies.

High-Performance Memristive Synapse Based on Space-Charge-Limited Conduction in LiNbO3

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO3/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (VO-VCM). The voltage-controlled VO-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of VO-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the VO-VCM-based Al/LiNbO3/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device.

Lattice Structures-Mechanical Description with Respect to Additive Manufacturing

Lattice structures, characterized by their repetitive, interlocking patterns, provide an efficient balance of strength, flexibility, and reduced weight, making them essential in fields such as aerospace and automotive engineering. These structures use minimal material while effectively distributing stress, providing high resilience, energy absorption, and impact resistance. Composed of unit cells, lattice structures are highly customizable, from simple 2D honeycomb designs to complex 3D TPMS forms, and they adapt well to additive manufacturing, which minimizes material waste and production costs. In compression tests, lattice structures maintain stiffness even when filled with powder, suggesting minimal effect from the filler material. This paper shows the principles of creating finite element simulations with 3D-printed specimens and with usage of the lattice structure. The comparing of simulation and real testing is also shown in this research. The efficiency in material and energy use underscores the ecological and economic benefits of lattice-based designs, positioning them as a sustainable choice across multiple industries. This research analyzes three selected structures-solid material, pure latices structure, and boxed lattice structure with internal powder. The experimental findings reveal that the simulation error is less than 8% compared to the real measurement. This error is caused by the simplified material model, which is considering the isotropic behavior of the used material PA12GB (not the anisotropic model). The used and analyzed production method was multi jet fusion.

Quantum Dots for Resistive Switching Memory and Artificial Synapse

Memristor devices for resistive-switching memory and artificial synapses have emerged as promising solutions for overcoming the technological challenges associated with the von Neumann bottleneck. Recently, due to their unique optoelectronic properties, solution processability, fast switching speeds, and low operating voltages, quantum dots (QDs) have drawn substantial research attention as candidate materials for memristors and artificial synapses. This review covers recent advancements in QD-based resistive random-access memory (RRAM) for resistive memory devices and artificial synapses. Following a brief introduction to QDs, the fundamental principles of the switching mechanism in RRAM are introduced. Then, the RRAM materials, synthesis techniques, and device performance are summarized for a relative comparison of RRAM materials. Finally, we introduce QD-based RRAM and discuss the challenges associated with its implementation in memristors and artificial synapses.

Dynamic memristor for physical reservoir computing

Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.

Two-dimensional material-based memristive devices for alternative computing

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.

Parametric Production of Prostheses Using the Additive Polymer Manufacturing Technology Multi Jet Fusion

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients' lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients.

Study on the Sodium-Doped Titania Interface-Type Memristor

Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for analog in-memory computing accelerators. However, the relatively low reliability of the memristor device and sneak current issues in CAA remain the main obstacles. Alkali ion-based interface-type memristors are promising solutions for implementing highly reliable memristor devices and neuromorphic hardware. This interface-type device benefits from self-rectifying and forming-free resistive switching (RS), and exhibits relatively low variation from device to device and cycle to cycle. In a previous report, we introduced an in situ grown Na/TiO2 memristor using atomic layer deposition (ALD) and proposed a RS mechanism from experimentally measured Schottky barrier modulation. Self-rectifying RS characteristics were observed by the asymmetric distribution of Na dopants and oxygen vacancies as the Ti metal used as the adhesion layer for the bottom electrode diffuses over the Pt electrode at 250 °C during the ALD process and is doped into the TiO2 layer. Here, we theoretically verify the modulation of the Schottky barrier at the TiO2/Pt electrode interface by Na ions. This study fabricated a Pt/Na/TiO2/Pt memristor device and confirmed its self-rectifying RS characteristics and stable retention characteristics for 24 h at 85 °C. Additionally, this device exhibited relative standard deviations of 27 and 7% in the high and low resistance states, respectively, in terms of cycle-to-cycle variation. To verify the RS mechanism, we conducted density functional theory simulations to analyze the impact of Na cations at interstitial sites on the Schottky barrier. Our findings can contribute to both fundamental understanding and the design of high-performance memristor devices for neuromorphic computing.

High-Reliability and Self-Rectifying Alkali Ion Memristor through Bottom Electrode Design and Dopant Incorporation

Ionic memristor devices are crucial for efficient artificial neural network computations in neuromorphic hardware. They excel in multi-bit implementation but face challenges like device reliability and sneak currents in crossbar array architecture (CAA). Interface-type ionic memristors offer low variation, self-rectification, and no forming process, making them suitable for CAA. However, they suffer from slow weight updates and poor retention and endurance. To address these issues, the study demonstrated an alkali ion self-rectifying memristor with an alkali metal reservoir formed by a bottom electrode design. By adopting Li metal as the adhesion layer of the bottom electrode, an alkali ion reservoir was formed at the bottom of the memristor layer by diffusion occurring during the atomic layer deposition process for the Na:TiO2 memristor layer. In addition, Al dopant was used to improve the retention characteristics by suppressing the diffusion of alkali cations. In the memristor device with optimized Al doping, retention characteristics of more than 20 h at 125 °C, endurance characteristics of more than 5.5 × 105, and high linearity/symmetry of weight update characteristics were achieved. In reliability tests on 100 randomly selected devices from a 32 × 32 CAA device, device-to-device and cycle-to-cycle variations showed low variation values within 81% and 8%, respectively.

3D simulation of conductive nanofilaments in multilayer h-BN memristors via a circuit breaker approach

A 3D simulation of conductive nanofilaments (CNFs) in multilayer hexagonal-BN memristors is performed. To do so, a simulation tool based on circuit breakers is developed including for the first time a 3D resistive network. The circuit breakers employed can be modeled with two, three and four resistance states; in addition, a series resistance and a module to account for quantum effects, by means of the quantum point contact model, are also included. Finally, to describe real dielectric situations, regions with a high defect density are modeled with a great variety of geometrical shapes to consider their influence in the resistive switching (RS) process. The simulator has been tuned with measurements of h-BN memristive devices, fabricated with chemical-vapour-deposition grown h-BN layers, which were electrically and physically characterized. We show the formation of CNFs that produce filamentary charge conduction in our devices. Moreover, the simulation tool is employed to describe partial filament rupture in reset processes and show the low dependence of the set voltage on the device area, which is seen experimentally.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Improved Resistive and Synaptic Characteristics in Neuromorphic Systems Achieved Using the Double-Forming Process

In this study, we investigate the electrical properties of ITO/ZrOx/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrOx-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I-V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule.

Advanced Composite Materials Utilized in FDM/FFF 3D Printing Manufacturing Processes: The Case of Filled Filaments

The emergence of additive manufacturing technologies has brought about a significant transformation in several industries. Among these technologies, Fused Deposition Modeling/Fused Filament Fabrication (FDM/FFF) 3D printing has gained prominence as a rapid prototyping and small-scale production technique. The potential of FDM/FFF for applications that require improved mechanical, thermal, and electrical properties has been restricted due to the limited range of materials that are suitable for this process. This study explores the integration of various reinforcements, including carbon fibers, glass fibers, and nanoparticles, into the polymer matrix of FDM/FFF filaments. The utilization of advanced materials for reinforcing the filaments has led to the enhancement in mechanical strength, stiffness, and toughness of the 3D-printed parts in comparison to their pure polymer counterparts. Furthermore, the incorporation of fillers facilitates improved thermal conductivity, electrical conductivity, and flame retardancy, thereby broadening the scope of potential applications for FDM/FFF 3D-printed components. Additionally, the article underscores the difficulties linked with the utilization of filled filaments in FDM/FFF 3D printing, including but not limited to filament extrusion stability, nozzle clogging, and interfacial adhesion between the reinforcement and matrix. Ultimately, a variety of pragmatic implementations are showcased, wherein filled filaments have exhibited noteworthy benefits in comparison to standard FDM/FFF raw materials. The aforementioned applications encompass a wide range of industries, such as aerospace, automotive, medical, electronics, and tooling. The article explores the possibility of future progress and the incorporation of innovative reinforcement materials. It presents a plan for the ongoing growth and application of advanced composite materials in FDM/FFF 3D printing.

Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing

This review article attempts to provide a comprehensive review of the recent progress in cerium oxide (CeO2)-based resistive random-access memories (RRAMs). CeO2 is considered the most promising candidate because of its multiple oxidation states (Ce3+ and Ce4+), remarkable resistive-switching (RS) uniformity in DC mode, gradual resistance transition, cycling endurance, long data-retention period, and utilization of the RS mechanism as a dielectric layer, thereby exhibiting potential for neuromorphic computing. In this context, a detailed study of the filamentary mechanisms and their types is required. Accordingly, extensive studies on unipolar, bipolar, and threshold memristive behaviors are reviewed in this work. Furthermore, electrode-based (both symmetric and asymmetric) engineering is focused for the memristor's structures such as single-layer, bilayer (as an oxygen barrier layer), and doped switching-layer-based memristors have been proved to be unique CeO2-based synaptic devices. Hence, neuromorphic applications comprising spike-based learning processes, potentiation and depression characteristics, potentiation motion and synaptic weight decay process, short-term plasticity, and long-term plasticity are intensively studied. More recently, because learning based on Pavlov's dog experiment has been adopted as an advanced synoptic study, it is one of the primary topics of this review. Finally, CeO2-based memristors are considered promising compared to previously reported memristors for advanced synaptic study in the future, particularly by utilizing high-dielectric-constant oxide memristors.

Resistive Switching Crossbar Arrays Based on Layered Materials

Resistive switching (RS) devices are metal/insulator/metal cells that can change their electrical resistance when electrical stimuli are applied between the electrodes, and they can be used to store and compute data. Planar crossbar arrays of RS devices can offer a high integration density (>10⁸  devices mm^{- 2} ) and this can be further enhanced by stacking them three-dimensionally. The advantage of using layered materials (LMs) in RS devices compared to traditional phase-change materials and metal oxides is that their electrical properties can be adjusted with a higher precision. Here, the key figures-of-merit and procedures to implement LM-based RS devices are defined. LM-based RS devices fabricated using methods compatible with industry are identified and discussed. The focus is on small devices (size < 9 µm² ) arranged in crossbar structures, since larger devices may be affected by artifacts, such as grain boundaries and flake junctions. How to enhance device performance, so to accelerate the development of this technology, is also discussed.

A Unified Current-Voltage Model for Metal Oxide-Based Resistive Random-Access Memory

Resistive random-access memory (RRAM) is essential for developing neuromorphic devices, and it is still a competitive candidate for future memory devices. In this paper, a unified model is proposed to describe the entire electrical characteristics of RRAM devices, which exhibit two different resistive switching phenomena. To enhance the performance of the model by reflecting the physical properties such as the length index of the undoped area during the switching operation, the Voltage ThrEshold Adaptive Memristor (VTEAM) model and the tungsten-based model are combined to represent two different resistive switching phenomena. The accuracy of the I-V relationship curve tails of the device is improved significantly by adjusting the ranges of unified internal state variables. Furthermore, the unified model describes a variety of electrical characteristics and yields continuous results by using the device's current-voltage relationship without dividing its fitting conditions. The unified model describes the optimized electrical characteristics that reflect the electrical behavior of the device.

Effects of Oxygen Flow Rate on Metal-to-Insulator Transition Characteristics in NbOx-Based Selectors

In this work, NbOx-based selector devices were fabricated by sputtering deposition systems. Metal-to-insulator transition characteristics of the device samples were investigated depending on the oxygen flow rate (3.5, 4.5, and 5.5 sccm) and the deposition time. The device stack was scanned by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). The yields, including MIT, nonlinear, and Ohmic, in working devices with different deposition conditions were also evaluated. Moreover, we observed the trend in yield values as a function of selectivity. In addition, the current-voltage (I-V) curves were characterized in terms of DC and pulse endurance. Finally, the switching speed and operating energies were obtained by applying a triangular pulse on the devices, and the recovery time and drift-free characteristics were obtained by the paired pulses.

Transformed Filaments by Oxygen Plasma Treatment and Improved Resistance State

The simple structure and operation method of resistive random-access memory (RRAM) has attracted attention as next-generation memory. However, as it is greatly influenced by the movement of oxygen atoms during switching, it is essential to minimize the damage and adjust the defects. Here, we fabricated an ITO/SnO_X/TaN device and investigated the performance improvement with the treatment of O₂ plasma. Firstly, the change in the forming curve was noticeable, and the defect adjustment was carried out effectively. By comparing the I-V curves, it was confirmed that the resistance increased and the current was successfully suppressed, making it suitable for use as a low-power consumption device. Retention of more than 10⁴ s at room temperature was measured, and an endurance of 200 cycles was performed. The filaments' configuration was revealed through the depth profile of X-ray photoelectron spectroscopy (XPS) and modeled to be visually observed. The work with plasma treatment provides a variety of applications to the neuromorphic system that require a low-current level.

Review of electrical stimulus methods of in situ transmission electron microscope to study resistive random access memory

Resistive random access memory (RRAM) devices have been demonstrated to be a promising solution for the implementation of a neuromorphic system with high-density synapses due to the simple device structure, nanoscale dimension, high switching speed, and low power consumption. Various electrical stimuli applied to RRAM devices could cause various working modes of the bionic synapses. The application of RRAM devices needs to understand the micromechanism of the resistive switching process, which is inseparable from advanced characterization techniques. In situ transmission electron microscopy (TEM) with high-resolution imaging and versatile external fields plays an important role in the static characterization and dynamic manipulation of nanoscale devices. Focused on in situ TEM techniques, this review article introduces in situ TEM setups and the corresponding sample fabrication process for RRAM research. Then, the electrical stimulating methodologies including pulse and direct current voltage applied to RRAM are introduced, followed by the summary of electron holography to characterize the electrical potential distribution. By applying various electrical stimuli to the RRAM samples, the working mode of bionic synapses could be changed according to the requirement. Finally, the outlook of the RRAM study with in situ TEM is proposed. This review demonstrates the electrical stimulus capability of in situ TEM to understand the physical mechanism of various types of RRAM devices.

Controlling resistive switching behavior in the solution processed SiO2-x device by the insertion of TiO2 nanoparticles

The resistive switching behavior of the solution processed SiO_x device was investigated by inserting TiO₂ nanoparticles (NPs). Compared to the pristine SiO_x device, the TiO₂ NPs inserted SiO_x (SiO_x@TiO₂ NPs) device achieves outstanding switching characteristics, namely a higher ratio of SET/RESET, lower operating voltages, improved cycle-to-cycle variability, faster switching speed, and multiple-RESET states. Density functional theory calculation (DFT) and circuit breaker simulation (CB) were used to detail the origin of the outstanding switching characteristic of the SiO_x@TiO₂ NPs. The improvement in resistive switching is mainly based on the difference in formation/rupture of the conductive path in the SiO₂ and SiO₂@TiO₂ NPs devices. In particular, the reduction of resistance and lower switching voltage of TiO₂ NPs control the formation and rupture of the conductive path to achieve more abrupt switching between SET/RESET with higher on/off ratio. This method of combined DFT calculation and CB offers a promising approach for high-performance non-volatile memory applications.

Memristor Neural Networks for Linear and Quadratic Programming Problems

This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are called memristor programming NNs (MPNNs), use a set of filamentary-type memristors with sharp memristance transitions for constraint satisfaction and an additional set of memristors with smooth memristance transitions for memorizing the result of a computation. The nonlinear dynamics and global optimization capabilities of MPNNs for QP and LP problems are thoroughly investigated via a recently introduced technique called the flux-charge analysis method. One main feature of MPNNs is that the processing is performed in the flux-charge domain rather than in the conventional voltage-current domain. This enables exploiting the unconventional features of memristors to obtain advantages over the traditional NNs for QP and LP problems operating in the voltage-current domain. One advantage is that operating in the flux-charge domain allows for reduced power consumption, since in an MPNN, voltages, currents, and, hence, power vanish when the quick analog transient is over. Moreover, an MPNN works in accordance with the fundamental principle of in-memory computing, that is, the nonlinearity of the memristor is used in the dynamic computation, but the same memristor is also used to memorize in a nonvolatile way the result of a computation.

Exploring Low Power and Ultrafast Memristor on p-Type van der Waals SnS

Memristor devices that exhibit high integration density, fast speed, and low power consumption are candidates for neuromorphic devices. Here, we demonstrate a filament-based memristor using p-type SnS as the resistive switching material, exhibiting superlative metrics such as a switching voltage ∼0.2 V, a switching speed faster than 1.5 ns, high endurance switching cycles, and an ultralarge on/off ratio of 10⁸. The device exhibits a power consumption as low as ∼100 fJ per switch. Chip-level simulations of the memristor based on 32 × 32 high-density crossbar arrays with 50 nm feature size reveal on-chip learning accuracy of 87.76% (close to the ideal software accuracy 90%) for CIFAR-10 image classifications. The ultrafast and low energy switching of p-type SnS compared to n-type transition metal dichalcogenides is attributed to the presence of cation vacancies and van der Waals gap that lower the activation barrier for Ag ion migration.

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V₂C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V₂C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V₂C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices.

Resistive switching devices have great promise for a wide variety of technological applications. Here, Yang et al demonstrate that electrochemically induced tellurium filament can give rise to resistive switching, and show that devices based on this can provide a number of advantages compared to metallic filament-based devices.

Stacked Two-Dimensional MXene Composites for an Energy-Efficient Memory and Digital Comparator

Two-dimensional MXene has enormous potential for application in industry and academia owing to its surface hydrophilicity and excellent electrochemical properties. However, the application of MXene in optoelectronic memory and logical computing is still facing challenges. In this study, an optoelectronic resistive random access memory (RRAM) based on silver nanoparticles (Ag NPs)@MXene-TiO₂ nanosheets (AMT) was prepared through a low-cost and facile hydrothermal oxidation process. The fabricated device exhibited a typical bipolar switching behavior and controllable SET voltage. Furthermore, we successfully demonstrated a 4-bit in-memory digital comparator with AMT RRAMs, which can replace five logic gates in a traditional approach. The AMT-based digital comparator may open the door for future integrated functions and applications in optoelectronic data storage and simplify the complex logic operations.

Supercritical Ammoniation-Enabled Interfacial Polarization for Function-Mode Transformation and Overall Optimization of Thin-Film Transistors

Thin-film transistors (TFTs) have drawn widespread applications in the increasingly sophisticated display field. Despite the mature process of fabricating enhancement-mode TFTs, lack of facile methods to realize depletion-mode TFTs restrains the implementation of complementary-type circuits, which in turn leads to relatively high power. Here, the supercritical fluid technique is introduced to elaborately design and tune the interface, providing the opportunity for function-mode transformation of TFTs. By harnessing supercritical-assisted ammoniation (SCA) treatment, interfacial polarization induces negative shift of threshold voltage (from 0.2 to -9.8 V), which allows TFTs to remain normally on-state in the absence of complex capacitor-integrated circuits. This convenient technique, without an additional manufacturing process to achieve function-mode transformation, can thus enable the fabrication of comprehensive-mode TFTs under the same process. Furthermore, comprehensive optimizations in the mobility (increases from 2.08 to 17.12 cm² V^-1 s^-1), leakage current (reduces from 1.33 × 10^-11 to 2.22 × 10^-12 A), hysteresis (reduces from 11.2 to 0.2 V), and on/off current ratio (increases from 9.65 × 10⁴ to 7.98 × 10⁶) are achieved simultaneously. Based on conjoint analysis of electrical and material characterization, a reaction model is established for a clearer understanding of the interfacial polarization process. Overall, this low-temperature SCA treatment offers an environmentally benign strategy to modulate the function mode of electronic devices via interfacial engineering and optimize device performance at the same time, exhibiting promise in promoting the implementation of complementary, low-power circuit.

Optoelectronic Artificial Synapses Based on Two-Dimensional Transitional-Metal Trichalcogenide

The memristor is a foundational device for an artificial synapse, which is essential to realize next-generation neuromorphic computing. Herein, an optoelectronic memristor based on a two-dimensional (2D) transitional-metal trichalcogenide (TMTC) is designed and demonstrated. Owing to the excellent optical and electrical characteristics of titanium trisulfide (TiS₃), the memristor exhibits stable bipolar resistance switching (RS) as a result of the controllable formation and rupturing of the conductive aluminum filaments. Multilevel storage is realized with light of multiple wavelengths between 400 and 808 nm, and the synaptic properties such as conduction modulation and spiking timing-dependent plasticity (STDP) are achieved. On the basis of the photonic potentiation and electrical habitual ability, Pavlovian-associative learning is successfully established on this TiS₃-based artificial synapse. All these results reveal the large potential of 2D TMTCs in artificial neuromorphic chips.

Asymmetric Bipolar Resistive Switching of Halide Perovskite Film in Contact with TiO2 Layer

Halide perovskite materials such as methylammonium lead iodide (CH₃NH₃PbI₃) have attracted considerable interest for the resistive random-access memory applications, which exploit a dramatic change in the resistance by an external electric bias. In many semiconductor films, the drift, accumulation, and chain formation of defects explain the change in the resistance by an external bias. This study demonstrates that the interface of CH₃NH₃PbI₃ with TiO₂ has a significant impact on the formation and rupture of defect chains and causes the asymmetric bipolar resistive switching in the Au/CH₃NH₃PbI₃/TiO₂/FTO device (FTO = fluorine-doped tin oxide). When a negative bias is applied to the Au electrode, iodine interstitials with the lowest migration activation energy move toward TiO₂ in the CH₃NH₃PbI₃ layer and pile up at the CH₃NH₃PbI₃-TiO₂ interface. Under the same condition, oxygen vacancies in the TiO₂ layer also travel to the CH₃NH₃PbI₃-TiO₂ interface and strongly attract iodine interstitials. As a result, a Schottky barrier appears at the CH₃NH₃PbI₃-TiO₂ interface, and the resistance of Au/CH₃NH₃PbI₃/TiO₂/FTO becomes much larger than that of Au/CH₃NH₃PbI₃/FTO in the high resistance state. The frequency dependence of the capacitance confirms the asymmetric appearance of a large space charge polarization at the CH₃NH₃PbI₃-TiO₂ interface, which causes the unique bipolar resistive switching behavior with the on/off ratio (10³) and retention time (>10⁴ seconds) at -0.85 V in Au/CH₃NH₃PbI₃/TiO₂/FTO film.

Ti3C2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti₃C₂ MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti₃C₂ MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti₃C₂ MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti₃C₂/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti₃C₂/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti₃C₂ MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti₃C₂ MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.

Recent Trends, Construction and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review

In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.

Variable-temperature activation energy extraction to clarify the physical and chemical mechanisms of the resistive switching process

This study investigates the physical and chemical mechanisms during the resistive switching process by means of obtaining the activation energy in the reaction procedure. From the electrochemical and electrical measurement analysis results of HfO2-based resistive random access memory (RRAM), it can be observed that the chemical reaction during the reset process is consistent with the first-order reaction law. The activation energy, Ea, is determined from the reaction rate constant k under a varying-temperature environment in the reset process. The whole reset chemical reaction process can be divided into five phases involving N-O bond breaking, O-O bond breaking and triple-step oxygen ion migration. The methodology of the activation energy determination carried out in this study showcases a distinct approach to elucidate the resistive switching mechanism of RRAM and offers insight into RRAM design for future potential application.

Type-I Core-Shell ZnSe/ZnS Quantum Dot-Based Resistive Switching for Implementing Algorithm

Core-shell semiconductor quantum dots (QDs) are one of the biggest nanotechnology successes so far. In particular, type-I QDs with straddling band offset possess the ability to enhance the charge carriers capturing which is useful for memory application. Here, the type-I core-shell QD-based bipolar resistive switching (RS) memory with anomalous multiple SET and RESET processes was demonstrated. The synergy and competition between space charge limited current conduction (arising from charge trapping in potential well of type-I QDs) and electrochemical metallization (ECM, originating from redox reaction of Ag electrode) process were employed for modulating the RS behavior. Through utilizing stochastic RS mechanisms in QD-based devices, four situations of RS behaviors can be classified into three states in Markov chain for implementing the application of a true random number generator. Furthermore, a 6 × 6 cross-bar array was demonstrated to realize the generation of random letters with case distinction.

Modulating the filament rupture degree of threshold switching device for self-selective and low-current nonvolatile memory application

Resistive switching devices have tremendous potential for memory, logic, and neuromorphic computing applications. Cation-based resistive switching devices intrinsically show nonvolatile memory characteristics under high compliance current (I _CC), while show volatile threshold switching (TS) selector characteristics under low I _CC. However, separate researches about cation-based memory or selector are hard to evade the typical current-retention dilemma, which results in the hardship to obtain low-current memory and high-current selector. Here, we propose a novel strategy to realize nonvolatile storage characteristics in a volatile TS device by modulating the rupture degree of conductive filament (CF). Enlarging the rupture degree of the CF with a certain RESET process, as confirmed by transmission electron microscope and energy dispersive spectrometry results, the threshold voltage of the Ag/HfO₂/Pt TS devices can be enlarged from 0.9 to 2.8 V. Generation of the voltage difference enables the volatile TS devices the ability of self-selective nonvolatile storage. Increasing the RESET magnitude and shrinking the device size have been proved effective ways to increase the read window of the TS memory (TSM) devices. Evading the limit of the current-retention dilemma, ultra-low energy dissipation can be obtained by decreasing I _CC to nA level. With self-selective, low-energy, and potential high-density integration characteristics, the proposed TSM device can act as a potential supplement of novel storage class memories.

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.

Self-Compliance and High Performance Pt/HfOx/Ti RRAM Achieved through Annealing

A self-compliance resistive random access memory (RRAM) achieved through thermal annealing of a Pt/HfO_x/Ti structure. The electrical characteristic measurements show that the forming voltage of the device annealing at 500 °C decreased, and the switching ratio and uniformity improved. Tests on the device’s cycling endurance and data retention characteristics found that the device had over 1000 erase/write endurance and over 10⁵ s of lifetime (85 °C). The switching mechanisms of the devices before and after annealing were also discussed.

An indirect way to achieve comprehensive performance improvement of resistive memory: when hafnium meets ITO in an electrode

Emerging resistive random access memory has attracted extensive research enthusiasm. In this study, an indirect way to improve resistive random access memory (RRAM) comprehensive performance through electrode material re-design without intensive switching layer engineering is presented by adopting a hafnium-indium-tin-oxide composite. Working parameters of the device can be effectively improved: not only are low operation power consumption and high working stability achieved, but the memory window is significantly enlarged, accompanied by an automatic self-current-compliance function. The correlation between hafnium incorporation and performance improvements and the corresponding current conduction mechanisms have been thoroughly investigated to clarify the resistive switching behavior and to explain the oxygen absorption buffer effect. The hafnium atom, with large atomic radius, is surrounded by soft electron clouds and has high chemical activity to attract oxygen ions. It facilitates the accumulation of more oxygen ions around the interface of the top electrode and the resistive switching layer, leading to lower current and Schottky conduction. This study presents an important strategy for designing and developing electrode materials to improve the characteristics of RRAM and offers an indirect method to modify device working behaviors, also unveiling a promising prospect for its potential future application in low-power information storage and calculation technology.

Low Power Consumption Nanofilamentary ECM and VCM Cells in a Single Sidewall of High-Density VRRAM Arrays

The technologies of 3D vertical architecture have made a major breakthrough in establishing high-density memory structures. Combined with an array structure, a 3D high-density vertical resistive random access memory (VRRAM) cross-point array is demonstrated to efficiently increase the device density. Though electrochemical migration (ECM) resistive random access (RRAM) has the advantage of low power consumption, the stability of the operating voltage requires further improvements due to filament expansions and deterioration. In this work, 3D-VRRAM arrays are designed. Two-layered RRAM cells, with one inert and one active sidewall electrode stacked at a cross-point, are constructed, where the thin film sidewall electrode in the VRRAM structure is beneficial for confining the expansions of the conducting filaments. Thus, the top cell (Pt/ZnO/Pt) and the bottom cell (Ag/ZnO/Pt) in the VRRAM structure, which are switched by different mechanisms, can be analyzed at the same time. The oxygen vacancy filaments in the Pt/ZnO/Pt cell and Ag filaments in the Ag/ZnO/Pt cell are verified. The 40 nm thickness sidewall electrode restricts the filament size to nanoscale, which demonstrates the stability of the operating voltages. Additionally, the 0.3 V operating voltage of Ag/ZnO/Pt ECM VRRAM demonstrates the potential of low power consumption of VRRAM arrays in future applications.

A unipolar nonvolatile resistive switching behavior in a layered transition metal oxide

Two-dimensional layered materials have been considered as promising candidates for resistive random access memory, one of the most promising next-generation nonvolatile memories. However, due to the types of defects, most of the devices still suffer from poor environmental stability, defects inducing complexity, and uncontrollability. Here, we fabricate memory cells based on synthesized high-quality two-dimensional layered transition-metal oxide (α-MoO₃) nanosheets which can be thinned to 8.68 nm (∼6 layers) and find a unipolar nonvolatile resistive switching behavior. Driven by the migration of intrinsic oxygen vacancies, the devices show a large memory window (∼10⁵), good memory voltage stability, long-term endurance (for durations of over 3 days and 50 manual DC switching cycles) and multi-bit memory states. Furthermore, we find the devices with an excellent temperature tolerance of lower SET/RESET voltages and a larger memory window (>10⁴ at 380 K) at higher temperatures, suggesting their potential in practical applications. Finally, all 2D memory devices are demonstrated using graphene/α-MoO₃/graphene heterostructures.

Detection of bovine serum albumin using hybrid TiO2 + graphene oxide based Bio - resistive random access memory device

Bio - molecules detection and their quantification with a high precision is essential in modern era of medical diagnostics. In this context, the memristor device which can change its resistance state is a promising technique to sense the bio - molecules. In this work, detection of the Bovine Serum Albumin (BSA) protein using resistive switching memristors based on TiO2 and TiO2 + graphene oxide (GO) is explored. The sensitivity of BSA detection is found to be 4 mg/mL. Both the devices show an excellent bipolar resistive switching with an on/off ratio of 73 and 100 respectively, which essentially demonstrates that the device with GO, distinguishes the resistance states with a high precision. The enhanced performance in the GO inserted device (~ 650 cycles) is attributed to the prevention of multi-dimensional and random growth of conductive paths.

Direct Observation of Structural Deformation Immunity for Understanding Oxygen Plasma Treatment-Enhanced Resistive Switching in HfOx-Based Memristive Devices

Oxygen ions' migration is the fundamental resistive switching (RS) mechanism of the binary metal oxides-based memristive devices, and recent studies have found that the RS performance can be enhanced through appropriate oxygen plasma treatment (OPT). However, the lack of experimental evidence observed directly from the microscopic level of materials and applicable understanding of how OPT improves the RS properties will cause significant difficulties in its further application. In this work, we apply scanning probe microscope (SPM)-based techniques to study the OPT-enhanced RS performance in prototypical HfOx based memristive devices through in situ morphology and electrical measurements. It is first found that the structural deformations in HfOx nanofilm induced by migration of oxygen ions and interfacial electrochemical reactions can be recovered by OPT effectively. More importantly, such structural deformations no longer occur after OPT due to the strengthening in lattice structure, which directly illustrates the enhanced quantity of HfOx nanofilm and the nature of enhanced RS properties after OPT. Finally, the underlying mechanisms of OPT-enhanced RS performance are analyzed by the results of X-ray photoelectron spectroscopic (XPS) surface analysis. In the OPT-enhanced HfOx nanofilm, oxygen vacancies in crystalline regions can be remarkably reduced by active oxygen ions' implantation. The oxygen ions transport will depend considerably on the grain boundaries and OPT-enhanced lattice structure will further guarantee the stability of conductive filaments, both of which ensure the uniformity and repeatability in RS processes. This study could provide a scientific basis for improving RS performance of oxides-based memristive devices by utilizing OPT.

A Novel Resistive Switching Identification Method through Relaxation Characteristics for Sneak-path-constrained Selectorless RRAM application

Resistive random access memory (RRAM) is a leading candidate in the race towards emerging nonvolatile memory technologies. The sneak path current (SPC) problem is one of the main difficulties in crossbar memory configurations. RRAM devices with desirable properties such as a selectorless, 1R-only architecture with self-rectifying behavior are potential SPC solutions. In this work, the intrinsic nonlinear (NL) characteristics and relaxation characteristics of bilayer high-k/low-k stacked RRAMs are presented. The intrinsic nonlinearity reliability of bilayer selectorless 1R-only RRAM without additional switches has been studied for their ability to effectively suppress SPC in RRAM arrays. The relaxation properties with resistive switching identification method by utilizing the activation energy (Ea) extraction methodology is demonstrated, which provides insights and design guidance for non-uniform bilayer selectorless 1R-only RRAM array applications.

Mediation in the second-order synaptic emulator with conductive atomic force microscopy

Memristors have been extensively studied for synaptic simulation and neuromorphic computation. Instead of focusing on implementing specific synaptic learning rules by carefully engineering external programming parameters, researchers recently have paid more attention to taking advantage of the second-order memristor that is more analogous to biologic synapses and modulated not only by external inputs but also by internal mechanisms. However, experimental evidence is still scarce. Here, we explore a BiMnO3 memristor by applying simple spike forms. The filament evolution dynamics, including processes of forming and spontaneous decay, were directly observed by the conductive atomic force microscopy (c-AFM) technique. We propose that the unique conductance state of the BMO memristor is regulated by the oxygen vacancy (VO) dynamic process. We believe this primary result is helpful to improve understanding of the internal mechanisms of the second-order oxide memristor, which exhibits promising application in building selectors, memories and neuromorphic-computing systems.

Configurable multi-state non-volatile memory behaviors in Ti3C2 nanosheets

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component. The Ti3C2-based RRAM exhibited typical bipolar switching behavior, long retention characteristics, low SET voltage, good mechanical stability and excellent reliability. By adjusting different compliance currents in the SET process, multi-state information storage was achieved. The charge trapping assisting hopping process is considered to be the main mechanism of resistive switching for this fabricated Ti3C2-based RRAM, which was verified by conductive atomic force microscopy (C-AFM) and Kelvin probe force microscopy (KPFM). Moreover, this flexible Ti3C2-based RRAM, with good mechanical stability and long retention properties, was successfully fabricated on a plastic substrate. Ti3C2-based RRAMs may open the door to additional applications and functionalities, with high potential for application in flexible electronics.

Nonvolatile Memories Based on Graphene and Related 2D Materials

The pervasiveness of information technologies is generating an impressive amount of data, which need to be accessed very quickly. Nonvolatile memories (NVMs) are making inroads into high-capacity storage to replace hard disk drives, fuelling the expansion of the global storage memory market. As silicon-based flash memories are approaching their fundamental limit, vertical stacking of multiple memory cell layers, innovative device concepts, and novel materials are being investigated. In this context, emerging 2D materials, such as graphene, transition metal dichalcogenides, and black phosphorous, offer a host of physical and chemical properties, which could both improve existing memory technologies and enable the next generation of low-cost, flexible, and wearable storage devices. Herein, an overview of graphene and related 2D materials (GRMs) in different types of NVM cells is provided, including resistive random-access, flash, magnetic and phase-change memories. The physical and chemical mechanisms underlying the switching of GRM-based memory devices studied in the last decade are discussed. Although at this stage most of the proof-of-concept devices investigated do not compete with state-of-the-art devices, a number of promising technological advancements have emerged. Here, the most relevant material properties and device structures are analyzed, emphasizing opportunities and challenges toward the realization of practical NVM devices.

Biodegradable Natural Pectin-Based Flexible Multilevel Resistive Switching Memory for Transient Electronics

Transient electronics that can physically vanish in solution can offer opportunities to address the ecological challenges for dealing with the rapidly growing electronic waste. As one important component, it is desirable that memory devices combined with the transient feature can also be developed as secrecy information storage systems besides the above advantage. Resistive switching (RS) memory is one of the most promising technologies for next-generation memory. Herein, the biocompatible pectin extracted from natural orange peel is introduced to fabricate RS memory devices (Ag/pectin/indium tin oxides (ITO)), which exhibit excellent RS characteristics, such as forming free characteristic, low operating voltages (≈1.1 V), fast switching speed (<70 ns), long retention time (>10⁴ s), and multilevel RS behaviors. The device performance is not degraded after 10⁴ bending cycles, which will be beneficial for flexible memory applications. Additionally, instead of using acid solution, the Ag/pectin/ITO memory device can be dissolved rapidly in deionized water within 10 min thanks to the good solubility arising from ionization of its carboxylic groups, which shows promising application for green electronics. The present biocompatible memory devices based on natural pectin suggest promising material candidates toward enabling high-density secure information storage systems applications, flexible electronics, and green electronics.