Guest Editorial – Special Issue on ‘Memristors: Devices, Models, Circuits, Systems, and Applications’

Abstract

As the human society enters the era of ‘big data’, the capacity to generate large amounts of information grows exponentially. Cities across the world are relying upon and consistently generating massive amount of data – traffic navigations, parking managements, energy consumption rates, etc. Overall, the data double in volume every 2 years and are predicted to reach 44 zettabytes by 2020. It should be noted that among the newly created data, only less than 20% of them are well structured and can be readily analyzed by software, while more than 80% of the data are unstructured and cannot be easily recognized and analyzed by existing computers/programs. The high value attached to the high volume and large variety of unstructured data can only be extracted if a proper analysis is possible. In particular, intelligent hardware that spontaneously learns to sort out the structure and information of the data through unsupervised learning is required for the data analytics as the tags regarding the formats and structures of the target data are missing. Memristor devices embedded in pioneering nanoelectronic platforms represent the most promising key-enabling technology for the treatment of massive amount of data. The memristor, a two-terminal circuit element characterized by a nonlinear relation between the time integrals of current and voltage (i.e., the current and voltage momenta, aka charge and flux), was theoretically envisioned by Prof. L. O. Chua back in 1971. Features of the memristor proposed by Prof. Chua were found in a nanoscale film based on titanium dioxide in 2008 by a team of Hewlett Packard researchers led by S. Williams. This discovery has been recently followed by the experimental observation of some aspects of memristor behavior in other nanostructures. Memristor nanodevices typically adopt a metal/insulator/metal structure. The electrode materials and the switching layer are carefully designed so as to obtain desirable programming voltage, on/off ratio, power consumption, and device variation that enable fast, low-power yet reliable data analysis. Some of these physical devices are capable to reproduce the nonlinear dynamics of neural synapses with high level of accuracy: they may process and store information at the same time, they may occupy nanoscale volumes, they may be arranged on multi-layer crossbar array configurations ideally suited for parallel processing, they may consume very little power, and, most importantly, they may exhibit flux-controllable conductances reminiscent of the ion flow-tunable weights of neural synapses. As an additional benefit, this technology is also enabling non–volatile low-power memories, that are assumed to be one of the possible replacements for current data storage systems. The COST Action IC1401, ‘Memristors: Devices, Models, Circuits, Systems and Applications (MemoCiS)’, supported by COST (European Cooperation in Science and Technology) is aimed at bringing together researchers of different backgrounds to work in unison, so as to overcome multidisciplinary barriers that exist across the various domains associated with memristors. Most members of the Action work across the workgroups have contributed in this Special Issue and on the progress made with respect to ‘Memristor Device Technology’, ‘Memristor Theory, Modeling, and Simulation’, ‘Memristor-based Circuits’, and ‘Memristive Systems’ (which include bioinspired networks and memristive biosensors). Since 1974, the International Journal of Circuit Theory and Applications ‡ has been paying attention to bridge gap between the theoretical concept of memristor and its use in Engineering, Physics, and Material Science. This Special Issue on Memristors: Devices, Models, Circuits, Systems, and Applications is devoted to create a focused forum on the theory of memristor, analysis of complex dynamics in memristor-based circuits and systems, new solutions for memristor fabrication, and their integration in neuromorphic systems, logi