A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.

The future of electronics based on memristive systems

A relaxation oscillator incorporating nanoscale niobium dioxide memristors that exhibit both a current- and a temperature-controlled negative differential resistance produces chaotic dynamics that aid biomimetic computing. In recent years, grids of memristor devices, with their synapse-like dynamics and adaptable conductivity, have demonstrated neural-network-type implementations of analogue (non-Boolean) computing. Suhas Kumar et al. now explore the possibility of exploiting chaotic dynamics in highly nonlinear niobium dioxide memristor devices. This idea is inspired by the theory that biological neurons operate in a regime called 'the edge of chaos', which is thought to be key to the ability of the human brain to tackle complex information processing tasks with high efficiency. The authors demonstrate a controllable regime of chaotic self-oscillations in their devices and simulate a memristor grid that can solve a typical computationally hard task—a travelling salesman problem—with higher accuracy and efficiency than an approach that does not incorporate chaotic elements. Building artificial neural networks with chaotic oscillators based on single electronic devices provides an exciting direction for unconventional analogue computing. At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos1 that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains2,3,4,5,6,7. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos2, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems8. Thus, a source of controllable chaotic behaviour that can be incorporated into a neural-inspired circuit may be an essential component of future computational systems. Such chaotic elements have been simulated using elaborate transistor circuits that simulate known equations of chaos9,10,11,12, but an experimental realization of chaotic dynamics from a single scalable electronic device has been lacking5,6,13. Here we describe niobium dioxide (NbO2) Mott memristors each less than 100 nanometres across that exhibit both a nonlinear-transport-driven current-controlled negative differential resistance and a Mott-transition-driven temperature-controlled negative differential resistance. Mott materials have a temperature-dependent metal–insulator transition that acts as an electronic switch, which introduces a history-dependent resistance into the device. We incorporate these memristors into a relaxation oscillator14 and observe a tunable range of periodic and chaotic self-oscillations15. We show that the nonlinear current transport coupled with thermal fluctuations at the nanoscale generates chaotic oscillations. Such memristors could be useful in certain types of neural-inspired computation by introducing a pseudo-random signal that prevents global synchronization and could also assist in finding a global minimum during a constrained search. We specifically demonstrate that incorporating such memristors into the hardware of a Hopfield computing network can greatly improve the efficiency and accuracy of converging to a solution for computationally difficult problems.

Chaotic dynamics in nanoscale NbO 2 Mott memristors for analogue computing

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

A novel Ag/oxide-based threshold switching device with attractive features including ≈1010 nonlinearity is developed. High-resolution transmission electron microscopic analysis of the nanoscale crosspoint device suggests that elongation of an Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching.

Anatomy of Ag/Hafnia-Based Selectors with 10 10 Nonlinearity

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

A number of important commercial applications would benefit from the introduction of easily manufactured devices that exhibit current-controlled, or “S-type,” negative differential resistance (NDR). A leading example is emerging non-volatile memory based on crossbar array architectures. Due to the inherently linear current vs. voltage characteristics of candidate non-volatile memristor memory elements, individual memory cells in these crossbar arrays can be addressed only if a highly non-linear circuit element, termed a “selector,” is incorporated in the cell. Selectors based on a layer of niobium oxide sandwiched between two electrodes have been investigated by a number of groups because the NDR they exhibit provides a promisingly large non-linearity. We have developed a highly accurate compact dynamical model for their electrical conduction that shows that the NDR in these devices results from a thermal feedback mechanism. A series of electrothermal measurements and numerical simulations corroborate this model. These results reveal that the leakage currents can be minimized by thermally isolating the selector or by incorporating materials with larger activation energies for electron motion.

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An accurate locally active memristor model for S-type negative differential resistance in NbOx

A printhead die assembly includes first and second printhead die Each of the first and second die has a respective first surface including ink dispensing devices constructed thereon, and a respective second surface opposite the first surface Each of the first and second die includes nozzles for the ink dispensing devices The first and second die are positioned so that the second surface of the first die faces the second surface of the second die, and the nozzles of the first die are aligned with the nozzles of the second die

Ken Vandenberghe

Papers

An accurate locally active memristor model for S-type negative differential resistance in NbOx

Printhead die assembly