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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

The ability for artificially reproducing human brain type signals’ processing is one of the main challenges in modern information technology, being one of the milestones for developing global communicating networks and artificial intelligence. Electronic devices termed memristors have been proposed as effective artificial synapses able to emulate the plasticity of biological counterparts. Here we report for the first time a single crystalline nanowire based model system capable of combining all memristive functions – non-volatile bipolar memory, multilevel switching, selector and synaptic operations imitating Ca2+ dynamics of biological synapses. Besides underlying common electrochemical fundamentals of biological and artificial redox-based synapses, a detailed analysis of the memristive mechanism revealed the importance of surfaces and interfaces in crystalline materials. Our work demonstrates the realization of self-assembled, self-limited devices feasible for implementation via bottom up approach, as an attractive solution for the ultimate system miniaturization needed for the hardware realization of brain-inspired systems.

Acting as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms.

Memristive devices are considered one of the most promising candidates to overcome technological limitations for realizing next‐generation memories, logic applications, and neuromorphic systems in the modern nanoelectronics and information technology. Despite the continuous efforts, understanding the resistive switching mechanism underlying memristive/neuromorphic behavior still represents a challenge. Metal oxide nanowire‐based memristors appear suitable model systems for a deeper understanding of the involved physical/chemical phenomena due the possibility for localizing and investigating the switching mechanism. In practical aspects, nanowire‐based devices can be grown using a bottom‐up approach, thus being considered reliable candidates for going beyond the current scaling limitations of the top‐down approach by the standard lithography. In addition, taking into advantage of the high surface‐to‐volume ratio of these nanostructures, new device functionalities can be achieved by exploiting surface effects. In the literature, a variety of nanowire‐based devices such as single nanowires, nanowire arrays, and networks are reported to exhibit memristive behavior, explained by different switching mechanisms. This work provides a comparative review and a comprehensive analysis of device performances and physical phenomena responsible for memristive and neuromorphic behavior in such nanostructures. The analysis of the state‐of‐art of memristor devices based on nanowires and nanorods represent a milestone toward the development of nanowire‐based artificial neural networks.

The physical mechanism involved in resistive switching phenomena occurring in devices based on ZnO nanowire (NW) arrays may vary considerably, also depending on the structure of the switching layer. In particular, it is shown here that the formation of a ZnO base layer between the metallic catalyst substrate and the NW, which is typical of CVD-grown ZnO NW arrays, should not be neglected when explaining the switching physical mechanism. The structural and electrical properties of this layer are investigated after the mechanical removal of NWs. Electrical measurements were performed in the presence of NWs and, after their removal, showed that the base alone exhibits resistive switching properties. The proposed switching mechanism is based on the creation/rupture of an oxygen vacancies conductive path along grain boundaries of the polycrystalline base. The creation of the filament is facilitated by the high concentration of vacancies at the grain boundaries that are oriented perpendicularly to the electrodes, as a direct consequence of the ZnO growth along the c-axis of the wurtzite lattice.

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next‐generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic‐sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

The ability of modifying the surface properties plays a crucial role in nanostructures where the high surface-to-volume ratio is often exploited for a various range of applications. In this work, the anisotropic dissolution of ZnO nanowires (NWs) in water was analysed and a reversible method for the modification of the surface wetting properties based on electron beam irradiation