Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Abstract

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.

Metastable oxygen vacancy ordering state and improved memristive behavior in TiO2 crystals

“…1. Additionally, since V O plays a crucial role in the resistive switching of TiO 2 [11], we focused on the influence of the metastable ordering state on the memristive behavior of TiO 2 with a crystal called Sample TO-10. To this end, I-V curves were measured at different temperatures around the V O ordering temperature by sweeping the voltage in a 0 ?…”

“…In this work, the V O s' behavior of TiO 2 single crystals was studied in detail above room temperature. TiO 2 was used as a model system because the behavior of V O s in TiO 2 is important for memristive/resistive switching [11], photocatalytic applications [12], light absorption [13], and colossal dielectric permittivity [14]. The ordering state of V O s in TiO 2 , known as Magnéli phases with the generic formula Ti n O 2nÀ1 (n is integer), has been the subject of considerable research to date [15,16].…”

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

“…Despite a bright future prospective for the development of next‐generation artificial intelligence (AI) systems, it is hard for such rigid top‐down architectures to emulate most typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Alternative ways using unconventional systems consisting of many interacting nano‐parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits . However, these systems were unable to demonstrate bio‐realistic implementation of structural plasticity including reweighting and rewiring and spatio‐temporal processing of input signals similarly to our brain.…”

Optically Modulated Threshold Switching in Core–Shell Quantum Dot Based Memristive Device

“…[ 2–8 ] Two terminal memristor (the acronym of memory and resistor) is a promising technology matching with human brain‐like computing scheme with numerous superiorities of simple architecture, easy scalability, low cost and fast operation speed. [ 1,9–13 ] The applied external electrical input stimulates the ions migration in storage layer followed by materials reconfiguration, which eventually results in resistance change of device which can be abrupt (digital) or gradual (analog) depending on different physical systems. [ 14–17 ] The ions motion in memristors naturally emulates the biological functions of synapses while the programmable conductance can represent the synaptic weight.…”