Engineering incremental resistive switching in TaO_xbased memristors for brain-inspired computing

TLDR

Brain‑inspired neuromorphic computing seeks to replace conventional digital architectures with systems that emulate neural processing, and memristors with analog resistive switching are viewed as promising synaptic elements. We aim to engineer analog switching linearity in TaOx memristors by homogenizing filament growth and dissolution rates. This is accomplished by inserting an ion‑diffusion‑limiting layer at the TiN/TaOx interface. The ion‑diffusion‑limiting layer suppresses the two‑regime conductance modulation, yields more linear conductance changes, lowers power consumption, enables spike‑timing‑dependent plasticity, and provides a general strategy for optimizing memristor performance in neuromorphic applications.

Abstract

Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.