In Situ Observation of Low-Power Nano-Synaptic Response in Graphene Oxide Using Conductive Atomic Force Microscopy

Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors

Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I-V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I-V curves), as usual. Besides, the set and reset voltages were calculated from the complete I-V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.

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.

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.

Direct Imaging of Ion Migration in Amorphous Oxide Electronic Synapses with Intrinsic Analog Switching Characteristics

Amorphous metal oxides with analog resistive switching functions (i.e., continuous controllability of the electrical resistance) are gaining emerging interest due to their neuromorphic functionalities promising for energy efficient electronics. The mechanisms are currently attributed to field-driven migration of the constituent ions, but the applications are being hindered by the limited understanding of the physical mechanisms due to the difficulty in analyzing the causal ion migration, which occurs on a nanometer or even atomic scale. Here, the direct electrical transport measurement of analog resistive switching and ångström scale imaging of the causal ion migration is demonstrated in amorphous TaO_x (a-TaO_x) by conductive atomic force microscopy. Atomically flat thin films of a-TaO_x, which is a practical material for commercial resistive random access memory, are fabricated in this study, and the mechanisms of the three known types of analog resistive switching phenomena (current-dependent set, voltage-dependent reset, and time-dependent switching) are directly visualized on the surfaces. The observations indicate that highly analog type of resistive switching can be induced in a-TaO_x by inducing the continuous redox reactions for 2.0 < x < 2.5, which are characteristic of a-TaO_x. The measurements also demonstrate drastic control of the switching stochasticity, which is attributable to controlled segregation of a metastable a-TaO₂ phase. The findings provide direct clues for tuning the analog resistive switching characteristics of amorphous metal oxides and developing new functions for future neuromorphic computing.

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.

Nano-Memristors with 4 mV Switching Voltage Based on Surface-Modified Copper Nanoparticles

The development of memristors operating at low switching voltages <50 mV can be very useful to avoid signal amplification in many types of circuits, such as those used in bioelectronic applications to interact with neurons and nerves. Here, it is reported that 400 nm-thick films made of dalkyl-dithiophosphoric (DDP) modified copper nanoparticles (CuNPs) exhibit volatile threshold-type resistive switching (RS) at ultralow switching voltage of ≈4 mV. The RS is observed in small nanocells with a lateral size of <50 nm^-2 , during hundreds of cycles, and with an ultralow variability. Atomistic calculations reveal that the switching mechanism is related to the modification of the Schottky barriers and insulator-to-metal transition when ionic movement is induced via external bias. The devices are also used to model integrate-and-fire neurons for spiking neural networks and it is concluded that circuits employing DDP-CuNPs consume around ten times less power than similar neurons implemented with a memristor that switches at 40 mV.

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.

Non-Uniform Spline Quasi-Interpolation to Extract the Series Resistance in Resistive Switching Memristors for Compact Modeling Purposes

An advanced new methodology is presented to improve parameter extraction in resistive memories. The series resistance and some other parameters in resistive memories are obtained, making use of a two-stage algorithm, where the second one is based on quasi-interpolation on non-uniform partitions. The use of this latter advanced mathematical technique provides a numerically robust procedure, and in this manuscript, we focus on it. The series resistance, an essential parameter to characterize the circuit operation of resistive memories, is extracted from experimental curves measured in devices based on hafnium oxide as their dielectric layer. The experimental curves are highly non-linear, due to the underlying physics controlling the device operation, so that a stable numerical procedure is needed. The results also allow promising expectations in the massive extraction of new parameters that can help in the characterization of the electrical device behavior.