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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., One of the most enticing candidates for next-generation computing systems is the memristor. Memristor-based novel architectures have demonstrated considerable promise in replacing or augmenting traditional computing platforms based on the Von Neumann architecture, which faces many issues in the big-data era, as well as in newly developed neuromorphic tasks. Although the current classical computing architecture is unlikely to be abandoned in the foreseeable future, the growing trend of neuromorphic, quantum, and bio-inspired computing schemes calls for more specialized beyond Von Neumann platforms. Memristors showcase multiple advantages in terms of small area footprint, energy efficiency, high endurance, bio-compatibility, and their inherent synaptic and neuromorphic behavior. The topic of this work is to present the memristive devices that meet the requirements for the implementation of the novel beyond Von Neumann applications and examine their switching mechanism and material selection, as well as to conduct a performance comparison between the fabricated devices paving the way for future computing applications., Postprint (author's final draft)

Computing demands are growing rapidly as bigdata and artificial intelligence applications become increasingly tasking. Bio-inspired and quantum-based techniques are proving to be quite promising for the development of novel circuits and systems. These systems can contribute to the resolution of a wider variety of problems while also providing improvements to existing techniques. As the von Neumann architecture’s expected performance, which has been dominant for the past several decades, is now hindered by physical limitations, novel computing architectures, assisted by novel materials and circuit devices, are starting to emerge and provide promising results. The topic of this work is to examine the memory and computing capabilities of emergent memristor-based nanocircuits and demonstrate their advantages compared to their classical counterparts., Postprint (author's final draft)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The threshold switching effect is critical in memristor devices for a range of applications, from crossbar design reliability to simulating neuromorphic features using artificial neural networks. The rich inherit dynamics of a metallic conductive filament (CF) formation are thought to be linked to this characteristic. Simulating these dynamics is necessary to develop an accurate memristor model. In this work we present a compact memristor model that utilizes the drift, diffusion and thermo-diffusion effects. These three effects are taken into consideration to derive the switching behavior of a memristor. The resistance of a memristor is calculated based on the evolution of a truncated cone shaped filament. The objective of this model is to achieve a realistic integration of switching mechanisms of the memristor device, while minimizing the overhead on computing resources and being compatible with circuit design tools. The model incorporates the effect of thermo-diffusion on the switching pattern, providing a different perception of the ionic transport processes, which enable the unipolar switching. SPICE simulation results provide an exact match with experimental results of Metal-Insulator-Metal (MIM) memristive devices of Ag/Si2/SiO2.07/Pt nanoparticles (NPs) configuration., Peer Reviewed, Postprint (published version)