Temperature dependence of resistive switching behaviors in resistive random access memory based on graphene oxide film

Graphene-based RRAM devices for neural computing

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

Hybridization state transition-driven carbon quantum dot (CQD)-based resistive switches for bionic synapses

As an emerging carbon-based material, carbon quantum dots (CQDs) have shown unstoppable prospects in the field of bionic electronics with their outstanding optoelectronic properties and unique biocompatible characteristics. In this study, a novel CQD-based memristor is proposed for neuromorphic computing. Unlike the models that rely on the formation and rupturing of conductive filaments, it is speculated that the resistance switching mechanism of CQD-based memristors is due to the conductive path caused by the hybridization state transition of the sp² carbon domain and sp³ carbon domain induced by the reversible electric field. This avoids the drawback of uncontrollable nucleation sites leading to the random formation of conductive filaments in resistive switching. Importantly, it illustrates that the coefficient of variation (C_V) of the threshold voltage can be as low as -1.551% and 0.083%, which confirms the remarkable uniform switching characteristics. Interestingly, the Pavlov's dog reflection as an important biological behavior can be demonstrated by the samples. Finally, the accuracy recognition rate of MNIST handwriting can reach up to 96.7%, which is very close to the ideal number (97.8%). A carbon-based memristor based on a new mechanism presented provides new possibilities for the improvement of brain-like computing.

Temperature, detriment or advantage for memory emergence: the case of ZnO

Despite the widespread emergence of memory effects in solid systems, understanding basic microscopic mechanisms that trigger them is still puzzling. We report how ingredients of solid state transport in polycristalline systems, such as semiconductor oxides, become sufficient conditions for a memristive response that points to the natural emergence of memory, discernible under adequate set of driving inputs. The experimental confirmation of these trends will be presented along with a compact analytical theoretical picture that allows discerning the relative contribution of the main building blocks of memory and the effect of temperature, in particular. These findings can be extended to a vast universe of materials and devices, providing a unified physical explanation for a wide class of resistive memories, and pinpointing the optimal driving configurations for their operation.

Controllable high-performance memristors based on 2D Fe2GeTe3oxide for biological synapse imitation

Memristors are an important component of the next-generation artificial neural network, high computing systems, etc. In the past, two-dimensional materials based memristors have achieved a high performance and low power consumption, though one at the cost of the other. Furthermore, their performance can not be modulated frequently once their structures are fixed, which remains the bottleneck in the development. Herein, a series of forming free memristors are fabricated with the same Cu/Fe₃GeTe₂oxide/Fe₃GeTe₂/Al structure, yet the On/Off ratio and set voltage is modulated continuously by varying the oxidation time during fabrication. With an optimal oxidation time, a large On/Off ratio (1.58 × 10³) and low set voltage (0.74 V) is achieved in a single device. The formation and rapture of Al conductive filaments are found to be responsible for the memristors, and the filaments density and the cross-section area increase with the increase of current compliance, which achieves a higher On/Off ratio. The memristor can imitate basic biological synaptic functions using voltage pulses, demonstrating the potential for low-power consuming neuromorphic computing applications.

Graphene oxide based synaptic memristor device for neuromorphic computing

Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.

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

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

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

Graphene oxide (GO) resistive memories offer the promise of low-cost environmentally sustainable fabrication, high mechanical flexibility and high optical transparency, making them ideally suited to future flexible and transparent electronics applications. However, the dimensional and temporal scalability of GO memories, i.e., how small they can be made and how fast they can be switched, is an area that has received scant attention. Moreover, a plethora of GO resistive switching characteristics and mechanisms has been reported in the literature, sometimes leading to a confusing and conflicting picture. Consequently, the potential for graphene oxide to deliver high-performance memories operating on nanometer length and nanosecond time scales is currently unknown. Here we address such shortcomings, presenting not only the smallest (50 nm), fastest (sub-5 ns), thinnest (8 nm) GO-based memory devices produced to date, but also demonstrate that our approach provides easily accessible multilevel (4-level, 2-bit per cell) storage capabilities along with excellent endurance and retention performance-all on both rigid and flexible substrates. Via comprehensive experimental characterizations backed-up by detailed atomistic simulations, we also show that the resistive switching mechanism in our Pt/GO/Ti/Pt devices is driven by redox reactions in the interfacial region between the top (Ti) electrode and the GO layer.

Scalable production of graphene with tunable and stable doping by electrochemical intercalation and exfoliation

Electrochemical intercalation and exfoliation produces graphene with a finely tunable work function between 4.8 eV and 5.2 eV which enables a threefold increase in the performance of graphene electrodes.

Modulation of surface trap induced resistive switching by electrode annealing in individual PbS micro/nanowire-based devices for resistance random access memory

Bipolar resistive switching (RS) devices are commonly believed as a promising candidate for next generation nonvolatile resistance random access memory (RRAM). Here, two-terminal devices based on individual PbS micro/nanowires with Ag electrodes are constructed, whose electrical transport depends strongly on the abundant surface and bulk trap states in micro/nanostructures. The surface trap states can be filled/emptied effectively at negative/positive bias voltage, respectively, and the corresponding rise/fall of the Fermi level induces a variation in a degenerate/nondegenerate state, resulting in low/high resistance. Moreover, the filling/emptying of trap states can be utilized as RRAM. After annealing, the surface trap state can almost be eliminated completely; while most of the bulk trap states can still remain. In the devices unannealed and annealed at both ends, therefore, the symmetrical back-to-back Fowler-Nordheim tunneling with large ON/OFF resistance ratio and Poole-Frenkel emission with poor hysteresis can be observed under cyclic sweep voltage, respectively. However, a typical bipolar RS behavior can be observed effectively in the devices annealed at one end. The acquirement of bipolar RS and nonvolatile RRAM by the modulation of electrode annealing demonstrates the abundant trap states in micro/nanomaterials will be advantageous to the development of new type electronic components.