Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design. Resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck. This Review focuses on how the switching mechanisms and the resultant electrical properties lead to various computing applications.

Resistive switching materials for information processing

The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

A comprehensive review on emerging artificial neuromorphic devices

Van der Waals heterostructure based on layered two-dimensional (2D) materials offers unprecedented opportunities to create materials with atomic precision by design. By combining superior properties of each component, such heterostructure also provides possible solutions to address various challenges of the electronic devices, especially those with vertical multilayered structures. Here, we report the realization of robust memristors for the first time based on van der Waals heterostructure of fully layered 2D materials (graphene/MoS2-xOx/graphene) and demonstrate a good thermal stability lacking in traditional memristors. Such devices have shown excellent switching performance with endurance up to 107 and a record-high operating temperature up to 340oC. By combining in situ high-resolution TEM and STEM studies, we have shown that the MoS2-xOx switching layer, together with the graphene electrodes and their atomically sharp interfaces, are responsible for the observed thermal stability at elevated temperatures. A well-defined conduction channel and a switching mechanism based on the migration of oxygen ions were also revealed. In addition, the fully layered 2D materials offer a good mechanical flexibility for flexible electronic applications, manifested by our experimental demonstration of a good endurance against over 1000 bending cycles. Our results showcase a general and encouraging pathway toward engineering desired device properties by using 2D van der Waals heterostructures.

/pdf/robust-memristors-based-on-layered-two-dimensional-materials-1qbzmrrye0.pdf

Robust memristors based on layered two-dimensional materials

The memristor1,2 is a promising building block for next-generation non-volatile memory3, artificial neural networks4-7 and bio-inspired computing systems8,9. Organizing small memristors into high-density crossbar arrays is critical to meet the ever-growing demands in high-capacity and low-energy consumption, but this is challenging because of difficulties in making highly ordered conductive nanoelectrodes. Carbon nanotubes, graphene nanoribbons and dopant nanowires have potential as electrodes for discrete nanodevices10-14, but unfortunately these are difficult to pack into ordered arrays. Transfer printing, on the other hand, is effective in generating dense electrode arrays15 but has yet to prove suitable for making fully random accessible crossbars. All the aforementioned electrodes have dramatically increased resistance at the nanoscale16-18, imposing a significant barrier to their adoption in operational circuits. Here we demonstrate memristor crossbar arrays with a 2-nm feature size and a single-layer density up to 4.5 terabits per square inch, comparable to the information density achieved using three-dimensional stacking in state-of-the-art 64-layer and multilevel 3D-NAND flash memory19. Memristors in the arrays switch with tens of nanoamperes electric current with nonlinear behaviour. The densely packed crossbar arrays of individually accessible, extremely small functional memristors provide a power-efficient solution for information storage and processing.

pdf/memristor-crossbar-arrays-with-6-nm-half-pitch-and-2-nm-2glb1s7vdo.pdf

Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension.

By employing a precise method for locating and directly imaging the active switching region in a resistive random access memory (RRAM) device, a nanoscale conducting channel consisting of an amorphous Ta(O) solid solution surrounded by nearly stoichiometric Ta(2) O(5) is observed. Structural and chemical analysis of the channel combined with temperature-dependent transport measurements indicate a unique resistance switching mechanism.

/pdf/anatomy-of-a-nanoscale-conduction-channel-reveals-the-2nbf8b5rsu.pdf

Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor.

/pdf/anatomy-of-a-nanoscale-conduction-channel-reveals-the-dpm1qakbkq.pdf

Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High-Performance Memristor

This paper reports an interlaboratory comparison that evaluated a protocol for measuring and analysing the particle size distribution of discrete, metallic, spheroidal nanoparticles using transmission electron microscopy (TEM). The study was focused on automated image capture and automated particle analysis. NIST RM8012 gold nanoparticles (30 nm nominal diameter) were measured for area-equivalent diameter distributions by eight laboratories. Statistical analysis was used to (1) assess the data quality without using size distribution reference models, (2) determine reference model parameters for different size distribution reference models and non-linear regression fitting methods and (3) assess the measurement uncertainty of a size distribution parameter by using its coefficient of variation. The interlaboratory area-equivalent diameter mean, 27.6 nm ± 2.4 nm (computed based on a normal distribution), was quite similar to the area-equivalent diameter, 27.6 nm, assigned to NIST RM8012. The lognormal reference model was the preferred choice for these particle size distributions as, for all laboratories, its parameters had lower relative standard errors (RSEs) than the other size distribution reference models tested (normal, Weibull and Rosin-Rammler-Bennett). The RSEs for the fitted standard deviations were two orders of magnitude higher than those for the fitted means, suggesting that most of the parameter estimate errors were associated with estimating the breadth of the distributions. The coefficients of variation for the interlaboratory statistics also confirmed the lognormal reference model as the preferred choice. From quasi-linear plots, the typical range for good fits between the model and cumulative number-based distributions was 1.9 fitted standard deviations less than the mean to 2.3 fitted standard deviations above the mean. Automated image capture, automated particle analysis and statistical evaluation of the data and fitting coefficients provide a framework for assessing nanoparticle size distributions using TEM for image acquisition.

/pdf/particle-size-distributions-by-transmission-electron-1x7fceiqj6.pdf

Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study

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Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor.

Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High-Performance Memristor

Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study