Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function.

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devices have opened up promising opportunities for electronic circuit applications beyond energy storage. One representative example is ionic resistive-switching (RS) devices (also called memristive devices or memristors), [3][4][5] which can be used as a nonvolatile memory element due to its excellent performance such as high switching speed (<1 ns), [6] good retention (>10 years), [7] good endurance (>10 9 ), [8] as well as high device scalability. [9] The RS effect refers to the phenomenon that the resistance of a dielectric thin film, typically sandwiched between two electrodes, can be reversibly modulated by an electric field. [2] In ionic RS devices, the mechanism originates from the rearrangement of atoms/ions in the dielectric thin film through ionic drift/ diffusion and electrochemical processes that lead to the formation/rupture of nanoscale conductive paths (filaments) between the two electrodes. [10] Controlling these internal ionic processes will enable the design and implementation of better engineered devices with improved performance and reliability. Additionally, recent studies show that the internal ionic processes during RS can be used to faithfully emulate many biological effects that are critical for learning and memory, allowing efficient neuromorphic systems to be implemented using solid-state devices and networks. [11][12][13] Controlled ionic processes can also be used to directly modify the composition and/or structure of the material itself at the atomic scale, allowing new nanostructures to be built on the fly, in a reconfigurable fashion.In this review, we will first discuss the switching mechanism of ion-induced RS (memristive) effects in nanoscale solid-state thin films, followed by discussions on controlling and utilization of the ionic effects to implement biological synaptic functions, and to reduce device variation and improve device stability. New concepts of material tuning enabled by controlled ionic process will be introduced at the end. Ion-Induced RS EffectSo far, ion-driven RS effects have been observed in a large variety of materials, ranging from oxides, halides, to chalcogenides, etc. [2,[13][14][15] Based on the types of ions involved during the RS process, the switching mechanism can be divided to three categories: cation-driven RS effects, anion-driven RS effects, and cation and anion jointly driven RS effects.Advances in the understanding of nanoscale ionic processes in solid-state thin films have led to the rapid development of devices based on coupled ionic-electronic effects. For example, ion-driven resistive-switching (RS) devices have been extensively studied for future memory applications due to their excellent performance in terms of switching speed, endurance, retention, and scalability. Recent studies further suggest that RS devices are more than just resistors with tunable resistance; instead, they exhibit rich and complex internal ionic dynamics that equip them with native information-processing capabilities, particularly in the temporal domain. RS effec...

Manipulation is the most exciting feature of the non-contact atomic force microscopy technique as it allows building nanostructures on surfaces. Usually vertical manipulations are accompanied by an abrupt tip modification leading to a change of contrast. Here we report on low-temperature experiments demonstrating vertical manipulations of 'super'-Cu atoms on the p(2 Â 1) Cu(110):O surface, both extractions to and depositions from the tip, when the imaging contrast remains the same. These results are rationalized employing a novel and completely general method that combines density functional theory calculations for obtaining energy barriers as a function of tip height and a Kinetic Monte Carlo algorithm for studying the tip dynamics and extraction of manipulation statistics. The model reveals a novel multistep manipulation mechanism combining activated jumps of 'super'-Cu atoms to/from the tip with their drag by and diffusion on the tip.

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