Atomic Layer Deposited Hf0.5Zr0.5O2-based Flexible Memristor with Short/Long-Term Synaptic Plasticity

Conformational transitions in redissolved silk fibroin films and application for printable self-powered multistate resistive memory biomaterials

3D printing of water stable proteins with elastic properties offers a broad range of applications including self-powered biomedical devices driven by piezoelectric biomaterials. Here, we present a study on water-soluble silk fibroin (SF) films. These films were prepared by mixing degummed silk fibers and calcium chloride (CaCl2) in formic acid, resulting in a silk I-like conformation, which was then converted into silk II by redissolving in phosphate buffer (PBS). Circular dichroism, Raman and infrared (IR) spectroscopies were used to investigate the transitions of secondary structure in silk I and silk II as the pH of the solvent and the sonication time were changed. We showed that a solvent with low pH (e.g. 4) maintains the silk I β-turn structure; in contrast solvent with higher pH (e.g. 7.4) promotes β-sheet features of silk II. Ultrasonic treatment facilitates the transition to water stable silk II only for the SF redissolved in PBS. SF from pH 7.4 solution has been printed using extrusion-based 3D printing. A self-powered memristor was realized, comprising an SF-based electric generator and an SF 3D-printed memristive unit connected in series. By exploiting the piezoelectric properties of silk II with higher β-sheet content and Ca2+ ion transport phenomena, the application of an input voltage driven by a SF generator to SF 3D printed holey structures induces a variation from an initial low resistance state (LRS) to a high resistance state (HRS) that recovers in a few minutes, mimicking the transient memory, also known as short-term memory. Thanks to this holistic approach, these findings can contribute to the development of self-powered neuromorphic networks based on biomaterials with memory capabilities.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.

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.

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti_4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Conventional filamentary memristors are limited in dynamics by the high electric-field dependence of the conductive filament. Here, Jeong et al. presents a method which creates a cluster-type memristor, enabling large conductance range and long data retention.

Flexible and Stretchable Memristive Arrays for in-Memory Computing

With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.

Asymmetric carrier transport in flexible interface-type memristor enables artificial synapses with sub-femtojoule energy consumption

Flexible and transparent artificial synapses with extremely low energy consumption have potential for use in brain-like neuromorphic electronics. However, most of the transparent materials for flexible memristive artificial synapses were reported to show picojoule-scale high energy consumption with kiloohm-scale low resistance, which limits the scalability for parallel operation. Here, we report on a flexible memristive artificial synapse based on Cs₃Cu₂I₅ with energy consumption as low as 10.48 aJ (= 10.48 × 10^-18 J) μm^-2 and resistance as high as 243 MΩ for writing pulses. Interface-type resistive switching at the Schottky junction between p-type Cu₃Cs₂I₅ and Au is verified, where migration of iodide vacancies and asymmetric carrier transport owing to the effective hole mass is three times heavier than effective electron mass are found to play critical roles in controlling the conductance, leading to high resistance. There was little difference in synaptic weight updates with high linearity and 250 states before and after bending the flexible device. Moreover, the MNIST-based recognition rate of over 90% is maintained upon bending, indicative of a promising candidate for highly efficient flexible artificial synapses.

Superior and stable ferroelectric properties of hafnium-zirconium-oxide thin films deposited via atomic layer deposition using cyclopentadienyl-based precursors without annealing

HfO2-based ferroelectric thin films deposited via atomic layer deposition have been extensively studied as promising candidates for next-generation ferroelectric devices. The conversion of an amorphous Hf1-xZrxO2 film to the ferroelectric phase (non-centrosymmetric orthorhombic phase) has been achieved through annealing using a post-thermal process. However, in this study, we present the first report of ferroelectricity of hafnium-zirconium-oxide (HZO) thin films deposited via atomic layer deposition using cyclopentadienyl-based precursors without additional post-thermal processing. By increasing the deposition temperature using a cyclopentadienyl-based cocktail precursor, the conditions of the as-deposited HZO thin film to crystallize well with an orthorhombic phase were secured, and excellent ferroelectric properties with a large remanent polarization (2Pr ∼ 47.6 μC cm-2) were implemented without crystallization annealing. The as-deposited HZO thin film possessed very stable ferroelectric properties without a wake-up effect or significant fatigue up to 106 cycles. Futhermore, we demonstrated the applicability to devices using negative capacitance and non-volatile memory characteristics. This result suggests that a new strategy can be applied to ferroelectric devices where subsequent processing temperature constraints are required, such as back-end-of-line processes and ferroelectric-based flexible device applications.

Transparent HfO x -based memristor with robust flexibility and synapse characteristics by interfacial control of oxygen vacancies movement

Hafnium oxides (HfO _x ) based flexible memristors were fabricated on polyethylene naphtholate (PEN) substrates to simulate a variety of bio-synapse functions. By optimizing the manufacturing conditions of electrode and active films, it is proved that the TiN/HfO _x /W/ITO/PEN bilayer device has robust flexibility and can still be modulated after 2000 times of bending. The memristor device exhibits better symmetrical and linear characteristics with excellent uniformity at lower programming power consumption (∼38 μW). In addition, the essential synaptic behaviors have further been achieved in the devices, including the transition from short-term plasticity to long-term plasticity and spike time-dependent plasticity. Through the analysis of I-V curves and XPS data, a switching mechanism based on HfO _x /W interface boundary drift is constructed. It is revealed that the redox reaction caused by W intercalation can effectively regulate the content of oxygen vacancy in HfO _x . At the same time, bias-induced interfacial reactions will regulate the movement of oxygen vacancies, which emulates bio-synapse functions and improves the electrical properties of the device.

Resistive Switching Characteristic Improvement in a Single-Walled Carbon Nanotube Random Network Embedded Hydrogen Silsesquioxane Thin Films for Flexible Memristors

In this study, we evaluated the improved memristive switching characteristics of hydrogen silsesquioxane (HSQ) nanocomposites embedded with a single-walled carbon nanotube (SWCNT) random network. A low-temperature solution process was implemented using a flexible memristor device on a polyethylene naphthalate (PEN) substrate. The difference in the resistive switching (RS) behavior due to the presence of the SWCNT random network was analyzed by the current transport mechanism. Such a random network not only improves the RS operation but also facilitates a stable multilevel RS performance. The multiple-resistance states exhibited highly reliable nonvolatile retention properties over 104 s at room temperature (25 °C) and at a high temperature (85 °C), showing the possibility of an analog synaptic weight modulation. Consequently, the gradual weight potentiation/depression was realized through 3 × 102 synaptic stimulation pulses. These findings suggest that the embedded SWCNT random network can improve the synaptic weight modulation characteristics with high stability for an artificial synapse and hence can be used in future neuromorphic circuits.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Stable and Multilevel Data Storage Resistive Switching of Organic Bulk Heterojunction

Organic nonvolatile memory devices have a vital role for the next generation of electrical memory units, due to their large scalability and low-cost fabrication techniques. Here, we show bipolar resistive switching based on an Ag/ZnO/P3HT-PCBM/ITO device in which P3HT-PCBM acts as an organic heterojunction with inorganic ZnO protective layer. The prepared memory device has consistent DC endurance (500 cycles), retention properties (104 s), high ON/OFF ratio (105), and environmental stability. The observation of bipolar resistive switching is attributed to creation and rupture of the Ag filament. In addition, our conductive bridge random access memory (CBRAM) device has adequate regulation of the current compliance leads to multilevel resistive switching of a high data density storage.

Memristive Switching Characteristics in Biomaterial Chitosan-Based Solid Polymer Electrolyte for Artificial Synapse

This study evaluated the memristive switching characteristics of a biomaterial solid polymer electrolyte (SPE) chitosan-based memristor and confirmed its artificial synaptic behavior with analog switching. Despite the potential advantages of organic memristors for high-end electronics, the unstable multilevel states and poor reliability of organic devices must be overcome. The fabricated Ti/SPE-chitosan/Pt-structured memristor has stable bipolar resistive switching (BRS) behavior due to a cation-based electrochemical reaction between a polymeric electrolyte and metal ions and exhibits excellent endurance in 5 × 102 DC cycles. In addition, we achieved multilevel per cell (MLC) BRS I-V characteristics by adjusting the set compliance current (Icc) for analog switching. The multilevel states demonstrated uniform resistance distributions and nonvolatile retention characteristics over 104 s. These stable MLC properties are explained by the laterally intensified conductive filaments in SPE-chitosan, based on the linear relationship between operating voltage margin (ΔVswitching) and Icc. In addition, the multilevel resistance dependence on Icc suggests the capability of continuous analog resistance switching. Chitosan-based SPE artificial synapses ensure the emulation of short- and long-term plasticity of biological synapses, including excitatory postsynaptic current, inhibitory postsynaptic current, paired-pulse facilitation, and paired-pulse depression. Furthermore, the gradual conductance modulations upon repeated stimulation by 104 electric pulses were evaluated in high stability.

Multi-Level Analog Resistive Switching Characteristics in Tri-Layer HfO2/Al2O3/HfO2 Based Memristor on ITO Electrode

Atomic layer deposited (ALD) HfO2/Al2O3/HfO2 tri-layer resistive random access memory (RRAM) structure has been studied with a transparent indium tin oxide (ITO) transparent electrode. Highly stable and reliable multilevel conductance can be controlled by the set current compliance and reset stop voltage in bipolar resistive switching. Improved gradual resistive switching was achieved because of the interdiffusion in the HfO2/Al2O3 interface where tri-valent Al incorporates with HfO2 and produces HfAlO. The uniformity in bipolar resistive switching with Ion/Ioff ratio (>10) and excellent endurance up to >103 cycles was achieved. Multilevel conductance levels in potentiation/depression were realized with constant amplitude pulse train and increasing pulse amplitude. Thus, tri-layer structure-based RRAM can be a potential candidate for the synaptic device in neuromorphic computing.

Resistive Switching Behavior of Magnesium Zirconia Nickel Nanorods

To effectively improve the uniformity of switching behavior in resistive switching devices, this study developed magnesium zirconia nickel (MZN) nanorods grown on ITO electrodes through hydrothermal method. The field emission scanning electron microscope image shows the NR formation. Al/MZN NR/ITO structure exhibits forming-free and bipolar resistive switching behaviors. MZN NRs have relatively higher ON/OFF ratio and better uniformity compared with MZN thin film. The superior properties of MZN NRs can be attributed to its distinct geometry, which leads to the formation of straight and extensible conducting filaments along the direction of MZN NR. The results suggest the possibility of developing sol–gel NR-based resistive memory devices.

Room-temperature developed flexible biomemristor with ultralow switching voltage for array learning

As one of the emerging neuromorphic computing devices, memristors may break through the limitation of traditional computers with a von Neumann architecture. However, the development of flexible memristors is limited by the high-temperature fabrication process, large operating voltage and non-uniform distribution of resistance. The room-temperature process has attracted great attention due to its advantages of low thermal dissipation, low cost and excellent compatibility with flexible electronics. Here, we proposed a fully physical vapour deposition (PVD) process for fabricating a memristor without additional heat treatment. The device showed excellent resistive switching characteristics with ultralow set/reset voltages (0.48 V/-0.39 V), uniform distribution (10%/15%), stable retention characteristic, multilevel storage behavior and reliable flexibility (radius of 10 mm). With continuously modulated conductance, typical synaptic plasticities were simulated by our flexible biomemristor, including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) and learning-forgetting curve. Furthermore, the array learning behavior like that of the human brain was simulated with these trainable biomemristors. This study paves a new way for developing low-cost, wearable, neuromorphic computing electronics at room temperature and expands the applications of artificial synapse arrays.

Toward a Reliable Synaptic Simulation Using Al-Doped HfO2 RRAM

The potential in a synaptic simulation for neuromorphic computation has revived the research interest of resistive random access memory (RRAM). However, novel applications require reliable multilevel resistive switching (RS), which still represents a challenge. We demonstrate in this work the achievement of reliable HfO₂-based RRAM devices for synaptic simulation by performing the Al doping and the postdeposition annealing (PDA). Transmission electron microscopy and operando hard X-ray photoelectron spectroscopy results reveal the positive impact of Al doping on the formation of oxygen vacancies. Detailed I-V characterizations demonstrate that the 16.5% Al doping concentration leads to better RS properties of the device. In comparison with the other reported results based on HfO₂ RRAM, our devices with 16.5% Al-doping and PDA at 450 °C show better reliable multilevel RS (∼20 levels) performance and an increased on/off ratio. The 16.5% Al:HfO₂ sample with PDA at 450 °C shows good potentiation/depression characteristics with low pulse width (10 μs) along with a good On/Off ratio (>1000), good data retention at room temperature, and high temperature and good program/erase endurance characteristics with a pulse width of 50 ns. The synapse features including potentiation, depression, and spike time-dependent plasticity were successfully achieved using optimized Al-HfO₂ RRAM devices. Our results demonstrate the beneficial effects of Al doping and PDA on the enhancement of the performances of RRAM devices for the synaptic simulation in neuromorphic computing applications.

Insights into Multilevel Resistive Switching in Monolayer MoS2

The advent of two-dimensional materials has opened a plethora of opportunities in accessing ultrascaled device dimensions for future logic and memory applications. In this work, we demonstrate that a single layer of large-area chemical vapor deposition-grown molybdenum disulfide (MoS₂) sandwiched between two metal electrodes can be tuned to show multilevel nonvolatile resistive memory states with resistance values separated by 5 orders of magnitude. The switching process is unipolar and thermochemically driven requiring significant Joule heating in the reset process. Temperature-dependent electrical measurements coupled with semiclassical charge transport models suggest that the transport in these devices varies significantly in the initial (pristine) state, high resistance state, and low resistance state. In the initial state, the transport is a one-step direct tunneling (at low voltage biases) and Fowler Nordeim tunneling (at higher bias) with an effective barrier height of 0.33 eV, which closely matches the Schottky barrier at the MoS₂/Au interface. In the high resistive state, trap-assisted tunneling provides a reasonable fit to experimental data for a trap height of 0.82 eV. Density functional theory calculations suggest the possibility of single- and double-sulfur vacancies as the microscopic origins of these trap sites. The temperature-dependent behavior of the set and reset process are explained by invoking the probability of defect (sulfur vacancy) creation and mobility of sulfur ions. Finally, conductive atomic force microscopy measurements confirm that the multifilamentary resistive memory effects are inherent to a single-crystalline MoS₂ triangle and not necessarily dependent on grain boundaries. The insights suggested in this work are envisioned to open up possibilities for ultrascaled, multistate, resistive memories for next-generation digital memory and neuromorphic applications.

Improvement of Conductance Modulation Linearity in a Cu2+-Doped KNbO3 Memristor through the Increase of the Number of Oxygen Vacancies

The Pt/KNbO₃/TiN/Si (KN) memristor exhibits various biological synaptic properties. However, it also displays nonlinear conductance modulation with the application of identical pulses, indicating that it should be improved for neuromorphic applications. The abrupt change of the conductance originates from the inhomogeneous growth/dissolution of oxygen vacancy filaments in the KN film. The change of the filaments in a KN film is controlled by two mechanisms with different growth/dissolution rates: a redox process with a fast rate and an oxygen vacancy diffusion process with a slow rate. Therefore, the conductance modulation linearity can be improved if the growth/dissolution of the filaments is controlled by only one mechanism. When the number of oxygen vacancies in the KN film was increased through doping of Cu²⁺ ions, the growth/dissolution of the filaments in the Cu²⁺-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.