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A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of "task specific" neural networks that derive from this material tunability. [ABSTRACT FROM AUTHOR]

We show temporal and frequency information can be effectively encoded in memristive devices with inherent short-term dynamics. Ag/Ag2S/Pd based memristive devices with low programming voltage (~100 mV) were fabricated and tested. At weak programming conditions, the devices exhibit inherent decay due to spontaneous diffusion of the Ag atoms. When the devices were subjected to pulse train inputs emulating different spiking patterns, the switching probability distribution function diverges from the standard Poisson distribution and evolves according to the input pattern. The experimentally observed switching probability distributions and the associated cumulative probability functions can be well-explained using a model accounting for the shortterm decay effects. Such devices offer an intriguing opportunity to directly encode neural signals for neural information storage and analysis. [ABSTRACT FROM AUTHOR]

Memristors and memristive systems have been extensively studied for data storage and computing applications such as neuromorphic systems. To act as synapses in neuromorphic systems, the memristor needs to exhibit analog resistive switching (RS) behavior with incremental conductance change. In this study, we show that the dynamic range of the analog RS behavior can be significantly enhanced in a tantalum-oxide-based memristor. By controlling different state variables enabled by different physical effects during the RS process, the gradual filament expansion stage can be selectively enhanced without strongly affecting the abrupt filament length growth stage. Detailed physics-based modeling further verified the observed experimental effects and revealed the roles of oxygen vacancy drift and diffusion processes, and how the diffusion process can be selectively enhanced during the filament expansion stage. These findings lead to more desirable and reliable memristor behaviors for analog computing applications. Additionally, the ability to selectively control different internal physical processes demonstrated in the current study provides guidance for continued device optimization of memristor devices in general. [ABSTRACT FROM AUTHOR]

Resistive switching devices (RRAMs) have been proposed a promising candidate for future memory and neuromorphic applications. Central to the successful application of these emerging devices is the understanding of the resistance switching and failure mechanism, and identification of key physical parameters that will enable continued device optimization. In this study, we report detailed retention analysis of a TaOx based RRAM at high temperatures and the development of a microscopic oxygen diffusion model that fully explains the experimental results and can be used to guide future device developments. The device conductance in low resistance state (LRS) was constantly monitored at several elevated temperatures (above 300 °C), and an initial gradual conductivity drift followed by a sudden conductance drop were observed during retention failure. These observations were explained by a microscopic model based on oxygen vacancy diffusion, which quantitatively explains both the initial gradual conductance drift and the sudden conductance drop. Additionally, a non-monotonic conductance change, with an initial conductance increase followed by the gradual conductance decay over time, was observed experimentally and explained within the same model framework. Specifically, our analysis shows that important microscopic physical parameters such as the activation energy for oxygen vacancy migration can be directly calculated from the failure time versus temperature relationship. Results from the analytical model were further supported by detailed numerical multi-physics simulation, which confirms the filamentary nature of the conduction path in LRS and the importance of oxygen vacancy diffusion in device reliability. Finally, these high-temperature stability measurements also reveal the existence of multiple filaments in the same device. [ABSTRACT FROM AUTHOR]