Supramolecular Framework Constructed by Dendritic Nanopolymer for Stable Flexible Perovskite Resistive Random-Access Memory.

The 3D supramolecular framework (3D-SF) is constructed in this work through the hydrogen bond assisted self-assembly of spherical dendritic nanopolymer to regulate the flexibility, stability, and resistive switching (RS) performance of perovskite resistive random-access memory (RRAM). Herein, the 3D-SF network acts as the perovskite crystallization template to regulate the perovskite crystallization process due to its coordination interaction of functional groups with the perovskite grains, presenting the uniform, pinhole-free, and compact perovskite morphology for stable flexible RRAM. The 3D-SF network in situ stays at the perovskite intergranular boundaries to crosslink the perovskite grains. The RS performance of 3D-SF-modified perovskite RRAM device is evidently improved to the ON/OFF ratio of 105 , the cycle number of 500 times, and the data retention time of 104 s. The 50-days exposure of unencapsulated RRAM device at ambient environment still makes the ON/OFF ratio to be kept at ≈104 , indicating the potential of long-term stable multilevel storage in the high-density data storage. The bending action under different radius also does not change the RS performance due to the excellent bending-resistant ability of 3D-SF-modified perovskite film. This work explores a novel polymer additive strategy to construct the 3D supramolecular framework for stable flexible perovskite optoelectronic devices.

Impact of Top Electrodes (Cu, Ag, and Al) on Resistive Switching behaviour of Cu-rich Cu2 ZnSnS4 (CZTS) Ideal Kesterite.

Cu2 ZnSnS4 (CZTS) active material-based resistive random-access memory (RRAM) devices are investigated to understand the impact of three different Cu, Ag, and Al top electrodes. The dual resistance switching (RS) behaviour of spin coated CZTS on ITO/Glass is investigated up to 102 cycles. The stability of all the devices (Cu/CZTS/ITO, Ag/CZTS/ITO, and Al/CZTS/ITO) is investigated up to 103  sec in low- (LRS) and high- (HRS) resistance states at 0.2 V read voltage. The endurance up to 102 cycles with 30 msec switching width shows stable write and erase current. Weibull cumulative distribution plots suggest that Ag top electrode is relatively more stable for set and reset state with 33.61 and 25.02 shape factors, respectively. The charge carrier transportation is explained by double logarithmic plots, Schottky emission plots, and band diagrams, substantiating that at lower applied electric field intrinsic copper ions dominate in Cu/CZTS/ITO, whereas, at higher electric filed, top electrodes (Cu and Ag) dominate over intrinsic copper ions. Intrinsic Cu+ in CZTS plays a decisive role in resistive switching with Al electrode. Further, the impedance spectroscopy measurements suggest that Cu+ and Ag+ diffusion is the main source for the resistive switching with Cu and Ag electrodes.

High-performance resistive random access memory using two-dimensional electron gas electrode and its switching mechanism analysis.

In this study, a two-dimensional electron gas (2DEG), which is a conductive layer formed at the interface of Al2O3and TiO2, was used as an electrode for resistive random access memory (RRAM) and implemented in a cell size down to 30 nm. For an RRAM device comprising W/2DEG/TiO2/W, we confirmed that the dominant switching mechanism changed from interfacial to filamentary as the cell size decreased from 500 nm to 30 nm. Through analyses of changes in forming characteristics and conduction mechanisms in the low resistive state depending on the cell size, it was identified that the 2DEG acted as an oxygen-scavenging layer of TiO2during the resistive switching process. By comparing the switching characteristics of RRAM devices with and without 2DEG for a 30 nm cell size, we confirmed that a high-performance 2DEG RRAM was realized, with highly uniform current-voltage characteristics, a low operating voltage (∼1 V), and a high on/off ratio (>102). Finally, the applicability of the proposed device to a crossbar array was validated by evaluating 1S1R operation with an NbO2-based selector. Considering the improved switching uniformity, the 2DEG RRAM shows promise for high-density memory applications.

Solving the issue of increasing forming voltage during device miniaturization in hafnium oxide-based resistive access memory using high-ksidewall material.

An electrothermal coupling model of resistive random access memory (RRAM) was established based on the oxygen vacancy conduction mechanism. By resolving the partial differential equation for the coefficients, the variation process of the device resistance was simulated. In our studies, a device model was proposed which can accurately simulate the whole process of RRAM forming, reset, and set. Based on the established model, a new high dielectric constant (high-k) material (La2O3) is introduced as the sidewall material. The La2O3sidewall material can concentrate the electric field and helps to speed up the formation of conductive filaments. The La2O3sidewall can effectively reduce the forming voltage increase during the miniaturization process. Then, the influence of sidewall thermal conductivity on forming voltage is studied, and it is discovered that low thermal conductivity helps to reduce the model's forming voltage and increase the temperature concentration. These findings serve as a foundation for more studies on the choice of sidewall materials.

Unsupervised learning in hexagonal boron nitride memristor-based spiking neural networks.

Resistive random access memory (RRAM) is an emerging non-volatile memory technology that can be used in neuromorphic computing hardware to exceed the limitations of traditional von Neumann architectures by merging processing and memory units. Two-dimensional (2D) materials with non-volatile switching behavior can be used as the switching layer of RRAMs, exhibiting superior behavior compared to conventional oxide-based devices. In this study, we investigate the electrical performance of 2D hexagonal boron nitride (h-BN) memristors towards their implementation in spiking neural networks (SNN). Based on experimental behavior of the h-BN memristors as artificial synapses, we simulate the implementation of unsupervised learning in SNN for image classification on the Modified National Institute of Standards and Technology dataset. Additionally, we propose a simple spike-timing-dependent-plasticity (STDP)-based dropout technique to enhance the recognition rate in h-BN memristor-based SNN. Our results demonstrate the viability of using 2D-material-based memristors as artificial synapses to perform unsupervised learning in SNN using hardware-friendly methods for online learning.

Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho2O3) based synaptic RRAM device.

In this manuscript, we report on the paramagnetic Ho2O3-based synaptic resistive random-access memory device for the implementation of neuronal functionalities such as long-term potentiation, long-term depression and spike timing dependent plasticity respectively. The plasticity of the artificial synapse is also studied by varying pulse amplitude, pulse width, and pulse interval. In addition, we could classify handwritten Modified National Institute of Standards and Technology data set (MNIST) using a fully connected neural network (FCN). The device-based FCN records a high classification accuracy of 93.47% which is comparable to the software-based test accuracy of 97.97%. This indicates the highly optimized behavior of our synaptic device for hardware neuromorphic applications. Successful emulation of Pavlovian classical conditioning for associative learning of the biological brain is achieved. We believe that the present device consists the potential to utilize in neuromorphic applications.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network.

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Hafnium Oxide (HfO2 ) - A Multifunctional Oxide: A Review on the Prospect and Challenges of Hafnium Oxide in Resistive Switching and Ferroelectric Memories.

Hafnium oxide (HfO2 ) is one of the mature high-k dielectrics that has been standing strong in the memory arena over the last two decades. Its dielectric properties have been researched rigorously for the development of flash memory devices. In this review, the application of HfO2 in two main emerging nonvolatile memory technologies is surveyed, namely resistive random access memory and ferroelectric memory. How the properties of HfO2 equip the former to achieve superlative performance with high-speed reliable switching, excellent endurance, and retention is discussed. The parameters to control HfO2 domains are further discussed, which can unleash the ferroelectric properties in memory applications. Finally, the prospect of HfO2 materials in emerging applications, such as high-density memory and neuromorphic devices are examined, and the various challenges of HfO2 -based resistive random access memory and ferroelectric memory devices are addressed with a future outlook.

Highly-packed self-assembled graphene oxide film-integrated resistive random-access memory on a silicon substrate for neuromorphic application.

Resistive random-access memories (RRAMs) based on metal-oxide thin films have been studied extensively for application as synaptic devices in neuromorphic systems. The use of graphene oxide (GO) as a switching layer offers an exciting alternative to other materials such as metal-oxides. We present a newly developed RRAM device fabricated by implementing highly-packed GO layers on a highly doped Si wafer to yield a gradual modulation of the memory as a function of the number of input pulses. By using flow-enabled self-assembly, highly uniform GO thin films can be formed on flat Si wafers in a rapid and simple process. The switching mechanism was explored through proposed scenarios reconstructing the density change of the sp2cluster in the GO layer, resulting in a gradual conductance modulation. We analyzed that the current in a low resistance state could flow by tunneling or hopping via clusters because the distance between the sp2clusters in closely-packed GO layers is short. Finally, through a pattern-recognition simulation with a Modified National Institute of Standards and Technology database, the feasibility of using close-packed GO layers as synapse devices was successfully demonstrated.

Facile Achievement of Complementary Resistive Switching in Block Copolymer Micelle-Based Resistive Memories.

Interest in resistive random access memory (RRAM) has grown rapidly in recent years for realizing ultrahigh density data storage devices. However, sneak currents in these devices can result in misreading of the data, thus limiting the applicability of RRAM. Complementary resistive switching (CRS) memory consisting of two antiserial RRAMs can considerably reduce sneak currents; however, complicated device architectures and manufacturing processes still remain as challenges. Herein, an effective and simple approach for fabricating CRS memory devices using self-assembled block copolymer micelles is reported. Cu ions are selectively placed in the core of polystyrene-block-poly(2-vinylpyridine) spherical micelles, and a hexagonally packed micelle monolayer is prepared through spin-coating. The micelle monolayer can be a symmetrical resistive switching layer, because the micelles and Cu act as dielectric and active metals in memory devices, respectively. The locally enhanced electric field and Joule heating achieved by the structured Cu atoms inside the micelles promote metal ionization and ion migration in a controlled manner, thus allowing for position selectivity during resistive switching. The micelle-based memory device exhibits stable and reliable CRS behavior, with a nonoverlapping and narrow distribution of threshold voltages. Therefore, this approach is promising for fabricating CRS memory devices for high-performance and ultrahigh-density RRAM applications.

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.

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices.

Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

Building memory devices from biocomposite electronic materials.

Natural biomaterials are potential candidates for the next generation of green electronics due to their biocompatibility and biodegradability. On the other hand, the application of biocomposite systems in information storage, photoelectrochemical sensing, and biomedicine has further promoted the progress of environmentally benign bioelectronics. Here, we mainly review recent progress in the development of biocomposites in data storage, focusing on the application of biocomposites in resistive random-access memory (RRAM) and field effect transistors (FET) with their device structure, working mechanism, flexibility, transient characteristics. Specifically, we discuss the application of biocomposite-based non-volatile memories for simulating biological synapse. Finally, the application prospect and development potential of biocomposites are presented.

Environmentally Robust Memristor Enabled by Lead-Free Double Perovskite for High-Performance Information Storage.

Memristors are emerging as a rising star of new computing and information storage techniques. However, the practical applications are severely challenged by their instability toward harsh conditions, including high moisture, high temperatures, fire, ionizing irradiation, and mechanical bending. In this work, for the first time, lead-free double perovskite Cs2 AgBiBr6 is utilized for environmentally robust memristors, enabling highly efficient information storage. The memory performance of the typical indium-tin-oxide/Cs2 AgBiBr6 /Au sandwich-like memristors is retained after 1000 switching cycles, 105 s of reading, and 104 times of mechanical bending, comparable to other halide perovskite memristors. Most importantly, the memristive behavior remains robust in harsh environments, including humidity up to 80%, temperatures as high as 453 K, an alcohol burner flame for 10 s, and 60 Co γ-ray irradiation for a dosage of 5 × 105 rad (SI), which is not achieved by any other memristors and commercial flash memory techniques. The realization of an environmentally robust memristor from Cs2 AgBiBr6 with a high memory performance will inspire further development of robust electronics using lead-free double perovskites.

Interface Engineering with MoS2 -Pd Nanoparticles Hybrid Structure for a Low Voltage Resistive Switching Memory.

Metal oxide-based resistive random access memory (RRAM) has attracted a lot of attention for its scalability, temperature robustness, and potential to achieve machine learning. However, a thick oxide layer results in relatively high program voltage while a thin one causes large leakage current and a small window. Owing to these fundamental limitations, by optimizing the oxide layer itself a novel interface engineering idea is proposed to reduce the programming voltage, increase the uniformity and on/off ratio. According to this idea, a molybdenum disulfide (MoS2 )-palladium nanoparticles hybrid structure is used to engineer the oxide/electrode interface of hafnium oxide (HfOx )-based RRAM. Through its interface engineering, the set voltage can be greatly lowered (from -3.5 to -0.8 V) with better uniformity under a relatively thick HfOx layer (≈15 nm), and a 30 times improvement of the memory window can be obtained. Moreover, due to the atomic thickness of MoS2 film and high transmittance of ITO, the proposed RRAM exhibits high transparency in visible light. As the proposed interface-engineering RRAM exhibits good transparency, low SET voltage, and a large resistive switching window, it has huge potential in data storage in transparent circuits and wearable electronics with relatively low supply voltage.

Analysis and Control of RRAM Overshoot Current.

To combat the large variability problem in RRAM, current compliance elements are commonly used to limit the in-rush current during the forming operation. Regardless of the compliance element (1R-1R or 1T-1R), some degree of current overshoot is unavoidable. The peak value of the overshoot current is often used as a predictive metric of the filament characteristics and is linked to the parasitic capacitance of the test structure. The reported detrimental effects of higher parasitic capacitance seem to support this concept. However, this understanding is inconsistent with the recent successes of compliance-free ultra-short pulse forming which guarantees a maximum peak overshoot current. We use detailed circuit analysis and experimental measurements of 1R-1R and 1T-1R structures to show that the peak overshoot is independent of the parasitic capacitance while the overshoot duration is strongly dependent on the parasitic capacitance. Forming control can be achieved, in ultra-short pulse forming, since the overshoot duration is always less than the applied pulse duration. The demonstrated success of ultra-short pulse forming becomes easier to reconcile after identifying the importance of overshoot duration.

Parasitic engineering for RRAM control.

The inevitable current overshoot which follows forming in filamentary RRAM devices is often perceived as a source of variability that should be minimized. This sentiment has led to efforts to curtail the overshoot by decreasing the parasitic capacitance using highly integrated 1T-1R or 1R-1R device structures. While this is readily achievable in single device test structures, it poses an intricate design constraint for memory array designs. Several papers (Degraeve et al., 2010, 2014; Fantini et al., 2013; Raghavan et al., 2013; Padovani et al., 2015) suggest that there is insufficient current to form stable filaments for small parasitic capacitances and/or low current compliance levels. Thus, the relationship between minimizing overshoot current and improved filament stability is tenuous. In this study, we utilize the forming energy-based understanding of filamentary forming to reveal that the parasitic capacitance should be optimized, rather than minimized for better filament control.

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

Reversible chemical and structural changes induced by ionic motion and reaction in response to electrical stimuli leads to resistive switching effects in metal-insulator-metal structures. Filamentary switching based on the formation and rupture of nanoscale conductive filament has been applied in non-volatile memory and volatile selector devices with low power consumption and fast switching speeds. Before the mass production of resistive switching devices, great efforts are still required to enable stable and reliable switching performances. The conductive filament, a bridge of microscopic metal-insulator-metal structure and macroscopic resistance states, plays an irreplaceable part in resistive switching behavior, as unreliable performance often originates from unstable filament behavior. In this Review, departing from the filamentary switching mechanism and the existing issues, recent advances of the switching performance improvement through the conductive filament modulation are discussed, in the sequence of material modulation, device structure design and switching operation scheme optimization. In particular, two-dimensional (2D) nanomaterials with excellent properties including and beyond graphene, are discussed with emphasis on performance improvement by their active roles as the switching layer, insertion layer, thin electrode, patterned electrode, and edge electrode, etc.

Non-Volatile ReRAM Devices Based on Self-Assembled Multilayers of Modified Graphene Oxide 2D Nanosheets.

2D nanomaterials have been actively utilized in non-volatile resistive switching random access memory (ReRAM) devices due to their high flexibility, 3D-stacking capability, simple structure, transparency, easy fabrication, and low cost. Herein, it demonstrates re-writable, bistable, transparent, and flexible solution-processed crossbar ReRAM devices utilizing graphene oxide (GO) based multilayers as active dielectric layers. The devices employ single- or multi-component-based multilayers composed of positively charged GO (N-GO(+) or NS-GO(+)) with/without negatively charged GO(-) using layer-by-layer assembly method, sandwiched between Al bottom and Au top electrodes. The device based on the multi-component active layer Au/[N-GO(+)/GO(-)]n /Al/PES shows higher ON/OFF ratio of ≈105 with switching voltage of -1.9 V and higher retention stability (≈104 s), whereas the device based on single component (Au/[N-GO(+)]n /Al/PES) shows ≈103 ON/OFF ratio at ±3.5 V switching voltage. The superior ReRAM properties of the multi-component-based device are attributed to a higher coating surface roughness. The Au/[N-GO(+)/GO(-)]n /Al/PES device prepared from lower GO concentration (0.01%) exhibits higher ON/OFF ratio (≈109 ) at switching voltage of ±2.0 V. However, better stability is achieved by increasing the concentration from 0.01% to 0.05% of all GO-based solutions. It is found that the devices containing MnO2 in the dielectric layer do not improve the ReRAM performance.

Imaging the Three-Dimensional Conductive Channel in Filamentary-Based Oxide Resistive Switching Memory.

Filamentary-based oxide resistive memory is considered as a disruptive technology for nonvolatile data storage and reconfigurable logic. Currently accepted models explain the resistive switching in these devices through the presence/absence of a conductive filament (CF) that is described as a reversible nanosized valence-change in an oxide material. During device operation, the CF cycles billion of times at subnanosecond speed, using few tens of microamperes as operating current and thus determines the whole device's performance. Despite its importance, the CF observation is hampered by the small filament size and its minimal compositional difference with the surrounding material. Here we show an experimental solution to this problem and provide the three-dimensional (3D) characterization of the CF in a scaled device. For this purpose we have recently developed a tomography technique which combines the high spatial resolution of scanning probe microscopy with subnanometer precision in material removal, leading to a true 3D-probing metrology concept. We locate and characterize in three-dimensions the nanometric volume of the conductive filament in state-of-the-art bipolar oxide-based devices. Our measurements demonstrate that the switching occurs through the formation of a single conductive filament. The filaments exhibit sizes below 10 nm and present a constriction near the oxygen-inert electrode. Finally, different atomic-size contacts are observed as a function of the programming current, providing evidence for the filament's nature as a defects modulated quantum contact.